Scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors

Abstract

To provide a system for scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors.SOLUTION: A system comprises a weather information station configured to provide precipitation rates for areas of a region, and a processor in communication with the weather information station. The processor is configured to acquire the precipitation rates of the areas of the region, determine a climatological metrics relationship between at least two climatological metrics of the precipitation rates, determine an operational metrics relationship between at least two operational metrics of the precipitation rate, and compare the climatological metrics relationship with the operational metrics relationship. The processor is also configured to determine at least one of a probability associated with a predetermined exceedance rate threshold and an exceedance rate threshold associated with a predetermined probability.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The embodiments described below relate to weather information, and more particularly, to scaling and statistical adjustment of precipitation rates for devices having sensors that are affected by precipitation. [Background technology]

[0002] background There are many different methods for measuring and / or predicting the amount or rate of precipitation. Each use case will determine which measurement or prediction method is most relevant. Often, a particular method involves integrating in either space or time. For example, weather radar typically provides second-scale samples taken every few minutes at a given spatial location, integrated over an area such as 1 square km. In contrast, rain gauges measure over areas smaller than 1 square meter, but typically integrate over 5-60 minutes before presenting a measurement. These scales and measurement times may be suitable for some use cases, such as traditional weather reporting to consumers.

[0003] However, some use cases are best served by a reliable estimate of the local (e.g., within 100 m) and instantaneous (e.g., within about 10-60 seconds) precipitation rate. The local instantaneous precipitation rate is equivalent, from a human perspective, to "how hard is it raining for me right now". Historically, climatological estimates of instantaneous precipitation rates were prepared by the telecommunications industry for use in planning outage percentages on microwave links. Climatological estimates of precipitation rates are typically available through organizations such as the International Telecommunications Union (ITU) and in academic literature. Telecommunications standards are local precipitation rates averaged over one minute.

[0004] However, the means to estimate fine-scale precipitation rates and their variability in real time in a comprehensive manner for a large number of locations over large areas are not available. Very few instruments are available to measure such precipitation rates, and weather models operate at significantly reduced spatial (multiple kilometers) and temporal (typically hourly) scales. In addition, such instruments may suffer from reliability issues and may lack sufficient spatial coverage to be suitable for purposes such as automated driving. Thus, there is a need for scaling and statistical adjustment of precipitation rates derived from widely available observations for purposes such as automated driving. Summary of the Invention

[0005] overview A system for scaling and statistical adjustment of precipitation rates for devices having sensors affected by precipitation. According to an embodiment, the system includes a weather information station configured to provide precipitation rates for an area of ​​a region and a processor in communication with the weather information station. The processor is configured to obtain precipitation rates for an area of ​​the region, determine a climatological metric relationship between at least two climatological metrics of the precipitation rates, determine an operational metric relationship between at least two operational metrics of the precipitation rates, and compare the climatological metric relationship to the operational metric relationship. The processor is also configured to determine at least one of a probability associated with a predetermined exceedance rate threshold and an exceedance rate threshold associated with the predetermined probability.

[0006] For scaling and statistical adjustment of precipitation rates for devices with sensors affected by precipitation A method is provided. According to one embodiment, the method includes obtaining precipitation rates for an area of ​​a region, determining a climatological metric relationship between at least two climatological metrics of the precipitation rates, determining an operational metric relationship between at least two operational metrics of the precipitation rates, and comparing the climatological metric relationship to the operational metric relationship. The method further includes determining at least one of a probability associated with a predetermined exceedance rate threshold and an exceedance rate threshold associated with the predetermined probability.

[0007] aspect According to one aspect, a system (100) for scaling and statistical adjustment of precipitation rates for devices having sensors affected by precipitation includes a weather information station (120) configured to provide precipitation rates for an area of ​​a region, and a processor (130) in communication with the weather information station (120). The processor (130) is configured to obtain precipitation rates for an area of ​​a region, determine a climatological metric relationship between at least two climatological metrics of the precipitation rates, determine an operational metric relationship between at least two operational metrics of the precipitation rates, and compare the climatological metric relationship to the operational metric relationship. The processor (130) is also configured to determine at least one of a probability associated with a predetermined exceedance rate threshold and an exceedance rate threshold associated with the predetermined probability.

[0008] Preferably, the at least two climatological metrics include maximum and average values ​​of climatological samples of precipitation rates for areas of the region, and the at least two operational metrics include maximum and average values ​​of operational samples of precipitation rates for areas of the region.

[0009] Preferably, the climatological metric relationship includes a relationship between maximum and average values ​​of climatological samples of precipitation rates for an area of ​​the region, and the operational metric relationship includes a relationship between maximum and average values ​​of operational samples of precipitation rates for an area of ​​the region.

[0010] Preferably, the probability associated with the predetermined exceedance rate threshold and the predetermined probability is the probability that the maximum precipitation rate in one or more sections of the region is greater than the predetermined exceedance rate threshold.

[0011] Preferably, the processor (130) is further configured to perform a spatial scale adjustment of the precipitation rates of the areas of the domain to the spatial scale of the operational domain.

[0012] Preferably, the processor (130) is further configured to perform a time scale adjustment of precipitation rates for areas of the region to the time scale of the operational period.

[0013] Preferably, the exceedance rate threshold is based on a maximum precipitation rate for a sensor within the vehicle. According to one aspect, a method for precipitation rate scaling and statistical adjustment for a device having a sensor affected by precipitation includes obtaining precipitation rates for an area of ​​a region, determining a climatological metric relationship between at least two climatological metrics of precipitation rates, determining an operational metric relationship between at least two operational metrics of precipitation rates, and comparing the climatological metric relationship to the operational metric relationship. The method also includes determining at least one of a probability associated with a predetermined exceedance rate threshold and an exceedance rate threshold associated with the predetermined probability.

[0014] Preferably, the at least two climatological metrics include maximum and average values ​​of climatological samples of precipitation rates for areas of the region, and the at least two operational metrics include maximum and average values ​​of operational samples of precipitation rates for areas of the region.

[0015] Preferably, the climatological metric relationship includes a relationship between a maximum and average value of a climatological sample of precipitation rates in an area of ​​the region, and the operational metric relationship includes a relationship between an operational sample of precipitation rates in an area of ​​the region. The relationship between the maximum and average values ​​of the samples is included.

[0016] Preferably, the probability associated with the predetermined exceedance rate and the predetermined probability is the probability that the maximum precipitation rate in one or more sections of the region is greater than a predetermined exceedance rate threshold.

[0017] Preferably, the method further comprises performing a spatial scale adjustment of the precipitation rates of the areas of the domain to the spatial scale of the operational domain.

[0018] Preferably, the method further comprises performing a time scale adjustment of precipitation rates for areas of the region to the time scale of the operational period.

[0019] Preferably, the exceedance rate threshold is based on a maximum precipitation rate for a sensor within the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS Like reference numbers represent like elements in all drawings. It should be understood that the drawings are not necessarily to scale. [Brief description of the drawings]

[0020] [Figure 1] 1 illustrates a system 100 for scaling and statistical adjustment of precipitation rates for vehicles having sensors that are subject to precipitation. [Diagram 2] 2 illustrates a vehicle electronics 200 configured to manage a control system based in part on determining a probability of exceeding a maximum precipitation rate for a sensor. [Diagram 3] 3 shows a diagram 300 illustrating how the metrics may be calculated. [Figure 4] 4 shows a flow chart 400 for determining the probability of exceeding a maximum precipitation rate for a sensor. [Diagram 5] 5 shows a chart 500 illustrating the spatial scale adjustment of observed precipitation rate values. [Figure 6] 6 shows a chart 600 illustrating a time scale adjustment from a relatively long measurement integration time to a sensor measurement integration time of 10 seconds. [Figure 7] 7 shows a chart 700 illustrating the probability distribution of a climatological metric relationship between maximum precipitation rate and average precipitation rate. [Figure 8]8 illustrates a method 800 for precipitation rate scaling and statistical adjustment for automated driving. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Detailed Description 1-8 and the following description depict specific examples to teach those skilled in the art how to make and use best mode embodiments of precipitation rate scaling and statistical adjustment for devices with sensors affected by precipitation. For the purpose of teaching the principles of the invention, some conventional aspects have been simplified or omitted. Those skilled in the art will appreciate variations from these examples that are within the scope of the description. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of precipitation rate scaling and statistical adjustment for devices with sensors affected by precipitation. As a result, the embodiments described below are not limited to the specific examples described below, but only by the claims and their equivalents.

[0022] FIG 1 illustrates a system 100 for scaling and statistical adjustment of precipitation rates for vehicles having sensors that are subject to precipitation. As shown in FIG 1, the system includes a weather station 110, an input weather forecast model 115, and a weather information station 120. As shown in FIG 1, the weather station 110 and the weather information station 120 are communicatively coupled. The weather information station 120 may be configured to receive fixed point observation data provided by the weather station 110. Additionally or alternatively, the weather information may be Station 120 may be configured to receive fixed point observation data provided by one or more other weather stations and model data from one or more other input weather forecast models.

[0023] As shown in FIG. 1, the system 100 also includes a processor 130 communicatively coupled to the weather information station 120. As shown in FIG. 1, the processor 130 is separate from the weather information station 120, although the processor 130 may be part of the weather information station 120. Although the system 100 is shown as including the weather station 110, the system 100 may be comprised of the weather information station 120 and the processor 130. The processor 130 may be configured to perform post-processing on one or more weather forecast models. More specifically, the processor 130 may be configured to determine a probability of exceeding a sensor precipitation rate threshold, as described in more detail below.

[0024] The system 100 may also include a vehicle 140 communicatively coupled to the processor 130. Although not shown, the vehicle 140 may alternatively or additionally be communicatively coupled to the weather information station 120 to receive or transmit data. For example, if the processor 130 is part of the weather information station 120, the vehicle 140 may be communicatively coupled to the weather information station 120. The data provided by the vehicle 140 may be mobile observation data. The vehicle 140 may also represent any device capable of providing mobile observation data.

[0025] The weather station 110 may be configured to determine weather conditions at or near the weather station 110. For example, the weather station 110 may include weather sensors, such as a barometer, a rain gauge sensor, a radio detection and ranging (RADAR), anemometer, humidity sensor, etc. Thus, the weather station 110 may be configured to provide stationary observation data. The stationary observation data may include a number of stationary observations available to the weather information station.

[0026] The input weather forecast models 115 are shown as communicatively coupled to the weather information station 120. The input weather forecast models 115 may include a weather forecast model for an area that includes the system 100, such as a portion of the system. Multiple input weather forecast models 115 may provide data to the weather information station 120.

[0027] Weather information station 120 may receive, aggregate, analyze, process, etc., weather information from weather station 110 and / or processor 130. For example, weather information station 120 may receive weather-related information from weather station 110, such as precipitation rates for areas in a region. The precipitation rates may have, for example, spatial and temporal scales associated with them. For example, the precipitation rates provided by weather station 110 may be for a relatively small area (e.g., 1 square meter) with a relatively large integration or accumulation time (e.g., 1 hour), such as typically obtained with a rain gauge, and / or for a relatively large area (e.g., 16 square kilometers) with a relatively short integration time (e.g., 5 seconds), such as typically obtained by using a weather radar.

[0028] Weather information station 120 may also provide weather-related information to weather station 110 and / or processor 130. For example, weather information station 120 may provide information about severe weather to weather station 110 so that weather station 110 may implement adjustments that are more appropriate for the severe weather. Weather information station 120 may or may not include processor 130.

[0029] Processor 130 may be configured to exchange data with vehicle 140. For example, processor 130 may be configured to receive data related to sensors within vehicle 140. As described in more detail below with reference to FIG. 2, vehicle 140 may provide a maximum precipitation rate for one or more sensors within the vehicle to processor 130 via weather information station 120. The maximum precipitation rate may be the rate of precipitation at which a sensor deteriorates such that a processor within vehicle 140 needs to make adjustments, such as relying more on other sensors, adjusting the speed and / or direction of vehicle 140, ceding at least a portion of control of vehicle 140 to the vehicle operator, etc.

[0030] FIGURE 2 illustrates vehicle electronics 200 configured to manage a control system based in part on a determination of a probability of exceeding a maximum precipitation rate. As shown in FIGURE 2, vehicle electronics 200 includes a sensor system 210 that is communicatively coupled to a vehicle control system 220. Vehicle control system 220 is communicatively coupled to a vehicle processing system 230. Vehicle processing system 230 is shown as including a vehicle processing system transceiver 230t that is communicatively coupled to an antenna 230a. Vehicle control system 220 is also communicatively coupled to a number of vehicle controllers 240.

[0031] The sensor system 210 is shown in FIG. 2 as being comprised of a number of sensors 212 communicatively coupled to a sensor processor 214. As shown in FIG. 2, the number of sensors 212 is comprised of a camera 212c, a light detection and ranging sensor (LIDAR) 212l, a radar detection and ranging sensor (RADAR) 212r, a global positioning system sensor (GPS) 212g, and a vehicle sensor 212v. More or fewer sensors may be used. For example, an alternative sensor system 210 may not use the LIDAR 212l and / or the RADAR 212r. Additionally or alternatively, other devices not shown in FIG. 2 may be employed.

[0032] The camera 212c, the LIDAR 212l, and the RADAR 212r may be configured to detect and provide sensor signals related to objects around a vehicle, such as the vehicle 140 described with respect to FIG. 1. That is, the camera 212c, the LIDAR 212l, and the RADAR 212r may be referred to as traffic sensors, obstacle sensors, and the like. For example, the camera may be configured to detect potential obstacles, such as pedestrians, other vehicles, bicycles, and the like, around the vehicle. The camera may also be configured to detect traffic control objects, such as traffic lights, stop signs, lane markings, road construction barriers, and the like. The LIDAR and / or the RADAR may be configured to detect objects and / or traffic control objects as well. Other objects, signals, and the like, related to or associated with the vehicle's environment may also be detected.

[0033] The vehicle sensors 212v may be sensors configured to detect and provide sensor signals related to a state of the vehicle, such as a throttle position sensor, an accelerometer, a braking force sensor, etc. The state of the vehicle may include a state related to a position, a speed, and / or an acceleration of the vehicle. The state of the vehicle may also include a position of a control device of the vehicle. Thus, the vehicle sensors 212v may provide sensor information regarding the state of the vehicle to determine, for example, when a braking force may be applied to avoid hitting an object, the amount of acceleration required to reach a certain speed within a desired time frame, etc.

[0034] The sensor processor 214 may be configured to receive and process sensor signals provided by the multiple sensors 212. For example, the sensor processor 214 may be configured to filter and buffer sensor signals, such as from the vehicle sensors 212v. In one example, the sensor processor 214 processes sensor signals provided by an accelerometer to remove noise and smooth data irregularities, such as drift and bias. The acceleration sensor signals may be integrated into a single accelerometer to detect and characterize transient acceleration events, perform averaging or bias analysis on multiple acceleration sensor signals, etc. For example, a vehicle tends to have zero lateral acceleration while stationary, which can be used to determine the relative acceleration bias between the accelerometers.

[0035] The sensor processor 214 may also perform processing on images acquired by the camera 212c, the LIDAR 212l, and / or the RADAR 212r. For example, the sensor processor 214 may be configured to perform object recognition algorithms on the sensor signals provided by the camera 212c to detect objects such as traffic lights. The sensor processor 214 may also correlate the sensor signals provided by the camera 212c, the LIDAR 212l, and / or the RADAR 212r. For example, the LIDAR 212l may be configured to detect a traffic light and provide a sensor signal including information about the traffic light. The sensor processor 214 may be configured to compare the sensor signals provided by the camera 212c and the LIDAR 212l and perform redundant object recognition algorithms thereon, and the like, to confirm that a detected object in the sensor signal provided by the camera 212c is a traffic light. The sensor processor 214 may also perform spatial determination algorithms that may use the LIDAR 212l to determine the position of a detected object relative to the LIDAR 212l, for example.

[0036] The sensor processor 214 may also be configured to provide one or more control signals to control the sensor 212. For example, the sensor processor 214 may provide a signal to the camera 212c to increase or decrease the ISO setting of the camera 212c to increase or decrease the sensitivity of the camera 212c to light. Additionally or alternatively, the sensor processor 214 may be configured to improve or optimize the ability of the LIDAR 212l to sense objects, for example, to increase or decrease the power level of a laser in the LIDAR 212l. Other configurations that result in improvement or optimization of the ability of the camera 212c, the LIDAR 212l, and the RADAR 212r to detect objects may be controlled by the sensor processor 214.

[0037] The ability of the camera 212c, the LIDAR 212l, and / or the RADAR 212r to sense objects may be improved or optimized by suitable configuration of the camera 212c, the LIDAR 212l, and / or the RADAR 212r. A suitable configuration may, for example, reduce noise in the sensor signal, increase the dynamic range, and / or optimize the contrast of the camera 212c, the LIDAR 212l, and / or the RADAR 212r. In an exemplary situation, during adverse weather conditions where ambient illumination is reduced and precipitation may scatter the laser beam emitted from the LIDAR 212l, a control signal provided by the sensor processor 214 may increase the ISO setting of the camera 212c and optimize the power of the laser beam emitted from the LIDAR 212l.

[0038] The sensor processor 214 may process sensor signals provided by the sensors 212 to provide sensor information. For example, the sensor processor 214 may process sensor signals provided by the camera 212c and the LIDAR 212l to determine sensor information related to the position of a traffic light relative to the vehicle. Additionally or alternatively, the sensor processor 214 may be configured to provide sensor information based on a command signal sent by the sensor processor 214 to one of the sensors 212. The sensor processor 214 may also receive and provide sensor information reflecting a state of one or more of the sensors 212. For example, the sensor processor 214 may be configured to provide an ISO setting of the camera 212c. Thus, the sensor processor 214 may be configured to provide the sensor information to the vehicle control system 220.

[0039] Vehicle control system 220 may be configured to receive and process sensor information from sensor system 210 to control the position, velocity, and / or acceleration of the vehicle. Vehicle control system 220 may be an autonomous, semi-autonomous, or partially automated control system for the vehicle. For example, vehicle control system 220 may be configured to perform processing required for one, some, or all of levels 1-5 of vehicle autonomy defined by the Society of Automotive Engineers (SAE). At level 1, the vehicle may have minimal automation, e.g., providing assistance for steering, braking, acceleration (cruise economy), etc. At level 5, vehicle control system 220 may fully control the vehicle without input from the driver. Vehicle control system 220 may process sensor information from sensor system 210 to automate some or all of the operation of the vehicle.

[0040] Thus, vehicle control system 220 may be configured to process sensor information to determine inputs to vehicle controller 240. For example, vehicle control system 220 may process vehicle speed, braking, acceleration, etc. provided by vehicle sensors 212v to determine, for example, that the speed (i.e., magnitude of speed) may need to be increased or decreased. Additionally or alternatively, vehicle control system 220 may also determine that the angular position of the steering shaft should be in a position to avoid, for example, a collision with an object. Other values ​​of variables related to vehicle acceleration, speed, steering, etc. may also be processed.

[0041] Vehicle control system 220 may also be configured to receive weather-related information from vehicle processing system 230, as described in more detail below. Vehicle control system 220 may perform processing on the sensor information based on the weather-related information. For example, vehicle control system 220 may determine that road conditions are likely slippery and may adjust acceleration and / or deceleration constraints for multiple vehicle control devices 240.

[0042] As shown in FIG. 2, the plurality of vehicle control devices 240 consists of steering 240s, throttle 240t, braking 240b, wipers 240w, and lights 240l controls, although more or fewer controls may be used. As can be seen, the vehicle control system 220 is communicatively coupled to each of the plurality of vehicle control devices 240. The steering 240s device may be an angular position control device that is part of the steering axis of the vehicle, an actuator that is not part of the steering axis, such as an actuator that is mechanically coupled to the axles and wheels of the vehicle, but any suitable control device that may control the direction of the vehicle may be used. The throttle 240t device may be configured to control the engine power of the vehicle. The engine may be an electric or internal combustion based engine, a hybrid of the two, or any other suitable engine that may provide power to increase and / or decrease the speed of the vehicle. The braking 240b devices may be actuators mechanically coupled to the wheels of the vehicle, such as fluid-operated brake calipers, but any suitable control device capable of slowing the vehicle may be used, such as energy recovery devices, electrically operated calipers, etc. The wiper 240w devices may be any suitable device that controls the removal of precipitation on a vehicle, such as the vehicle's windshield. The lighting 2401 devices may be any suitable device that can control the brightness, width, and / or direction of light, such as the vehicle's exterior lights.

[0043] Vehicle processing system 230 may be configured to receive weather-related information from antenna 230a via vehicle processing system transceiver 230t. Antenna 230a may be configured to receive weather-related information from, for example, weather information station 120. Weather information station 120 may provide any suitable weather-related information including current weather conditions and forecasted weather conditions. The weather information may be actual weather condition measurements of weather conditions, estimates of weather conditions, corrections to weather condition measurements provided by weather station 110, etc. The weather-related information may be determined, generated, calculated, etc. by processor 130 and provided by processor 130 to weather information station 120. The weather-related information may include, for example, Weather information may be provided by the weather station 120 to the vehicle processing system 230 via an antenna 230a and a vehicle processing system transceiver 230t.

[0044] The weather-related information provided to the vehicle processing system 230 may include a probability that a rate of precipitation at or near a vehicle, such as the vehicle 140 described with reference to FIG. 1, may be greater than one or more values. The probability that the rate of precipitation may be exceeded may be used to determine a probability of exceeding a maximum precipitation rate for a sensor. Thus, the processor 130 and / or the vehicle processing system 230 may determine a probability of exceeding a maximum precipitation rate for a sensor. The sensor may be one or more of the sensors 212 of the sensor system 210 described above, although any suitable maximum precipitation rate for a sensor may be used.

[0045] As described in more detail below, metrics may be calculated to perform scaling and statistical adjustments, such as spatial or temporal scaling adjustments of precipitation rates for devices having sensors that are affected by precipitation. For example, climate characteristic metrics may be calculated from the climate characteristic samples of precipitation rates, and these climate characteristic metrics may be used to statistically adjust precipitation rates for devices having sensors that are affected by precipitation, such as automotive vehicles, surface scanning equipment, surveying equipment, etc.

[0046] 3 shows a diagram 300 illustrating how the metrics may be calculated. As shown in FIG. 3, the diagram 300 includes a region 310, which includes zones 310a dividing the region 310. Each zone 310a has a corresponding average precipitation rate, which may be determined by observation or calculated to correspond to expected observation. The average precipitation rate for each zone 310a may be determined by accumulating precipitation values ​​over a period of time, which may be referred to as the measurement integration time.

[0047] Area 310a and subsequent area 310a' in time may have physical dimensions of approximately one square kilometer, although any suitable dimensions and / or units may be employed. Thus, area 310 and subsequent area 310' in time may have an area of ​​4 kilometers by 4 kilometers (km) or 16 square kilometers (sq-km). The values ​​of the precipitation rate for area 310 and subsequent area 310' in time are in units of millimeters per hour (mm / hr), although any suitable units may be used, even if measured or calculated for averaging (accumulation or integration) periods different from one hour.

[0048] Also shown is a subsequent region 310' having a subsequent area 310a' having the same spatial dimensions as region 310 but a different average precipitation rate than region 310. The measurement integration time used to calculate the average precipitation rate of the subsequent region 310' is different from the measurement integration time used to calculate the average precipitation rate of region 310. That is, the two measurement integration times may have the same span, but at different times. Diagram 300 also includes a sub-region 312 of region 310 consisting of four areas 310a adjacent to region of interest 320 in region 310, and a subsequent sub-region 312' consisting of four subsequent areas 310a' adjacent to region of interest 320 in the subsequent region 310'.

[0049] As described above, the area 310a and the temporally subsequent area 310a' divide the region 310 and the temporally subsequent region 310', respectively. As a result, the area 310a and the temporally subsequent area 310a' are adjacent and contiguous to each other, respectively. Although the region 310 and the temporally subsequent area 310' are shown as having a uniform 4×4 cubic area 310a and temporally subsequent area 310a' configuration, any suitable configuration may be adopted, such as non-uniform areas, but other alternative areas may also be adjacent and contiguous to each other.

[0050] Thus, the region 310 and the temporally subsequent region 310' may each be approximately ergodic with respect to weather. That is, for example, a time series of precipitation rates in one of the zones 310a may have the same statistics as the collection of surrounding zones 310a over a period of time. For example, a person standing at a fixed location will measure approximately the same range of precipitation rates over an hour as a group of people spread across different zones 310a in the region 310 would measure at any one moment during that hour. This may be due to weather cells moving through the region 310, causing variability in both space and time, but generally resulting in the same distribution characterization of precipitation rates in the different zones 310a in the region 310.

[0051] As can be seen, area 310 and subsequent area 310' are separated in time by five minutes. The average precipitation rates shown in area 310a and subsequent area 310a' may be determined over measurement integration times taken at different times, five minutes apart. For example, the average precipitation rate for area 310a may be averaged over a one minute period, as shown, although any suitable measurement integration time may be employed.

[0052] As can be seen, there may be some limitations on the average precipitation rate for the area 310a of the region 310 and the area 310a' of the area 310 that is subsequent in time and subsequent in time. For example, there may be incomplete sampling. The observations provide a small spatiotemporal sample of the precipitation rate that is often highly variable. It would be preferable to have hundreds or thousands of samples to estimate the peak rate within the observation area. This would allow one to characterize the distribution function of the precipitation rate and accurately assess the potential peak rate, which may be even larger than any of the observations. In this case, we only have 16 observations and must do our best to estimate the distribution function, and what its maximum likelihood value is. There may also be limitations related to spatial scaling. While the average precipitation rate spans 1 sq-km, we explore precipitation rates that may occur over a small spatial scale, on the order of about 100 square meters (sq-m). There may also be limitations related to time scaling. The average precipitation rates for region 310 and the temporally subsequent region 310' represent averages over 10 minutes, however, we explore precipitation rates that may vary on approximately 10-60 second scales.

[0053] To address this issue, we may use climatological samples with a much larger sample size to calculate the probabilities associated with precipitation rates. For example, observations may be stored in an archive as they are received by a weather information station, such as weather information station 120 described above. The archive may therefore include historical data, including radar data. This historical data may be the basis for the climatological sample of region 310. From this climatological sample, various statistical metrics may be calculated for a predefined sample criterion. The sample criterion may be, for example, the region 310 for which radar data is available. Calculations may be performed for all of region 310 (or other regions) or for smaller groups of region 310, such as sub-regions 312. Sub-regions 312 may correspond, for example, to cities of interest represented by region of interest 320. Statistical metrics may include, for example, mean, maximum, and / or variance per region, such as region 310 shown in FIG. 3, as well as relationships of variables, such as differences or ratios, calculated over some climatological time frame rather than an operational time frame. Metrics may potentially be calculated separately by season or other climatological factors.

[0054] As will be explained in more detail below, the climate characteristic samples may not be used to calculate local and instantaneous precipitation rates, but rather to determine probabilities associated with a given precipitation rate. For example, one may calculate the probability that the precipitation rate will exceed a selected precipitation rate, called the exceedance rate threshold, of 100 mm / hour over an area of ​​100 sq-m during a measurement integration time of 5 minutes. This probability may be referred to as the exceedance rate probability. This exceedance rate probability is , may be used for various decisions, such as decisions that may be based on knowing the probability that a vehicle sensor, such as sensor 212 described above, will operate properly during a five minute measurement integration period.

[0055] Thus, the climate characteristic metrics (such as mean, maximum, and / or variance) derived from the climate characteristic samples may be used in combination with observed values, such as the average precipitation rate for the region 310, to estimate the probability of an exceedance rate. That is, the climate characteristic metrics derived from the climate characteristic samples may be used to calculate the probability that the precipitation rate will exceed an exceedance rate threshold, or the rate at which the probability exceeds the exceedance rate probability within the region 310, given the observed values ​​for the region 310. This estimation may be performed operationally in real time. The combination of the climate characteristic metrics derived from the climate characteristic samples with the observed values ​​may be achieved as discussed in more detail below and subsequently with reference to FIGS. 4-7.

[0056] A sampling reconstruction may be performed. For an observed average precipitation rate of an area in a region, such as area 310a of region 310 described above, the maximum average precipitation rate in that region is, by definition, equal to or greater than the average of the average precipitation rate. The average of the average precipitation rates may be calculated. The average precipitation rate of area 310a of region 310 may also provide 16 samples to determine the variability of the average precipitation rate associated with the average precipitation rate of region 310. Calculating a relationship, such as a difference and / or ratio, between the maximum and the average of the average precipitation rate may provide an estimate of what the actual maximum would be if a metric from a climatological characteristic that may have a statistically significant sample size is used. That is, the relationship between the average and maximum of the average precipitation rate may have a statistical distribution that is subject to characterization with climatological data. Once the climatological distribution is characterized, that information may be used to estimate the probability of the actual maximum rate given the average precipitation rate of the region. This means that one can estimate the probability of the actual maximum that one would observe if one could measure at more locations within the area.

[0057] The observed variance within a region can be used to further refine this estimate, for example, based on whether it is larger or smaller than the variance determined using the climate characteristics. For example, separate climate characteristic metrics may be calculated for cases where the variance of the mean precipitation rate for a region is low and high. Application of this methodology allows for operational predictions such as the following: if the observed mean of the mean precipitation rate for a region is 2 mm / hr and the variance is high, then a maximum addition rate greater than 5 mm / hr anywhere in the region (totaling 7 mm / hr) may be expected less than 50% of the time, and a maximum addition rate greater than 10 mm / hr may be expected less than 5% of the time.

[0058] A spatial scale adjustment may also be performed. A spatial downscaling relationship calculated from the climatological characteristics may be used to relate the estimated maximum rates of a region to rates of a smaller area. This climatological relationship may be calculated by comparing average rates over sub-regions or regions of different sizes (e.g., 1 sq-km, 4 sq-km, 16 sq-km), as shown by region 310 and sub-region 312, and extrapolating to a smaller scale. An "extrapolation factor" (typically a small percentage increase) may then be applied to the estimated maximum precipitation rates determined in the sampling reconstruction step.

[0059] A time-scaling adjustment may also be performed. A temporal downscaling relationship calculated from the climatological properties is used to relate the estimated maximum rate of any temporal integration to the shorter measurement integration time. The temporal downscaling climatological relationship may be calculated by comparing the average value of the mean precipitation rate over the various measurement integration times and extrapolating to the smaller scale. An "extrapolation factor" (typically a small percentage increase) is then applied to the estimated maximum precipitation rate determined in the previous step. The specific case of radar observations In , this step may not be required since sampling already occurs on the second scale.

[0060] Using this methodology or system, estimates of current local instantaneous precipitation rates can be obtained from current observations. For forecasting to determine estimates of local instantaneous precipitation rates for future times, the observations are replaced by their forecasted estimates and the same methodology is followed. To increase the fidelity of the information, precipitation rate estimates obtained using this method can be calibrated against climate characteristic forecasts, such as those developed under International Telecommunications Union (ITU) standards or similar methodologies. An exemplary methodology is described below.

[0061] FIG. 4 illustrates a flowchart 400 for determining the probability of exceeding a sensor's maximum precipitation rate. As shown in FIG. 4, the flowchart 400 begins by acquiring precipitation data 410. The precipitation data 410 may be acquired, for example, by the weather station 110, although any suitable station may be employed. The flowchart 400 performs quality control 420 on the precipitation data, which may include discarding known bad data in the precipitation data, correcting for bias in the precipitation data, etc. The flowchart 400 illustrates that after the quality control 420 is performed on the precipitation data, an offline process 430 may be performed on the precipitation data. As shown in FIG. 4, the offline process 430 includes calculating climate characteristic statistics for sampling uncertainty 430a, calculating climate characteristic metrics for time integral dependence 430b, and calculating climate characteristic metrics for spatial scale dependence 430c. The offline process may be performed over a climatological time frame and thus may result in statistically significant metrics due to the relatively large sample size. The precipitation data may also be provided to a spatial scale adjustment 440 , a time scale adjustment 450 , and a sampling adjustment 460 to provide a probability 470 of exceeding an exceedance rate threshold 480 .

[0062] The precipitation data 410 may be obtained by one or more weather stations, such as the weather station 110 described above with reference to FIG. 1. The precipitation data may include data that is inaccurate. For example, the actual precipitation rate may be 80 mm / hr but is erroneously measured as 50 mm / hr. Thus, the quality control 420 may detect and remove inaccurate precipitation data. The quality control 420 may use any suitable method for detecting and removing inaccurate data, such as comparing the data to a moving average and removing data that is outside a threshold from the moving data.

[0063] As discussed above, the precipitation data 410 may include precipitation rates calculated over various spatial and temporal scales. For example, a RADAR measurement of a precipitation rate of 10 mm / hour may be integrated over, for example, 5 seconds. That is, during the measurement integration time of 5 seconds, the measured precipitation rate was 10 mm / hour. Similarly, the RADAR may have a spatial resolution associated with a precipitation rate of, for example, 1 sq-km, which may be much smaller than the spatial scale of an area in a region. As can be appreciated, the precipitation data 410 may need to be scaled temporally and spatially to relate to the maximum precipitation rate of the sensor.

[0064] To address this issue, precipitation data 410 provided by quality control 420 is shown as being provided to an offline process 430, as well as spatial scale adjustment 440, time scale adjustment 450, and sampling adjustment 460. The offline process 430 may calculate metrics such as the mean, maximum, and variance of the precipitation data at a climatological scale, such as over a multi-year period, and on a climatological basis, such as seasonal or monthly. For example, the offline process may use data spanning several years to calculate the average, maximum, and variance of the precipitation data for August ( The offline process 430 may calculate precipitation rate averages, maxima, and variances for sections of a region, such as the region 310 described above, on a climatological basis. Because of the sample size of the precipitation data relied upon, the metrics calculated by the offline process 430 may be statistically significant.

[0065] The metrics calculated by the offline process 430 may then be provided to a spatial scale adjustment 440, a time scale adjustment 450, and a sampling adjustment 460 to ensure that the output is statistically significant and “operational” or relevant to the current state of the vehicle.

[0066] As shown in FIG. 4, the spatial scale adjustment 440 may receive the calculated climatological metrics 430c and adjust the spatial scale of the region of interest, such as the region of interest 320 described with reference to FIG. 3, for spatial scale dependency. The spatial scale of the region of interest may be about 125 sq-m. The spatial scale of the region of interest may be, for example, an area where an object is detectable by the LIDAR 212l described with reference to FIG. 2. As mentioned above, the LIDAR 212l may have a maximum precipitation rate for the region of interest. That is, if the actual precipitation rate for the region of interest is greater than the maximum precipitation rate of the LIDAR 212l, the performance of the LIDAR 212l may be degraded. The spatial scale adjustment 440 of the region of interest is described in more detail below with reference to FIG. 5.

[0067] The time scale adjustment 450 may receive the calculated climatological metrics 430b and adjust the time scale of a region of interest, such as the region of interest 320 described with reference to FIG. 3, for time integral dependency. The time scale of the region of interest may be 10 seconds, but any suitable time scale may be used. The time scale of the region of interest may be a measurement integration time at which a maximum precipitation rate of the LIDAR 212l is specified. For example, the LIDAR 212l may have a maximum precipitation rate of 10 mm / hour specified for a 10 second integration interval. In other words, the performance of the LIDAR 212l may be sufficient if the actual precipitation rate, determined by integrating over a 10 second interval, is less than the maximum precipitation rate of the LIDAR 212l. The time scale adjustment 450 is described in more detail below with reference to FIG. 6.

[0068] 4, the sampling adjustment 460 may use the results of the offline process 430 of calculating climate characteristic statistics for sampling uncertainty 430a. More specifically, the sampling adjustment 460 may use an estimated maximum precipitation rate value for a measurement area and interval to determine the probability that the precipitation rate may exceed an exceedance rate threshold.

[0069] FIG. 5 illustrates a chart 500 showing spatial scale adjustments of observed precipitation rate values. As shown in FIG. 5, chart 500 includes an observed precipitation rate axis 510 and a predicted precipitation rate axis 520, which are in units of mm / hr, although any suitable units may be used. Chart 500 also includes a measured area relationship 530. Measured area relationship 530 relates observed precipitation rate values ​​calculated for observed precipitation rate measurement areas to predicted maximum precipitation rates calculated for maximum precipitation rate measurement areas. Measured area relationship 530 is comprised of a plot 530a for a measured area of ​​16 sq-km, a plot 530b for a measured area of ​​1 sq-km, and a plot 530c for a measured area of ​​125 sq-m.

[0070] The 16 sq-km measured area plot 530a and the 1 sq-km measured area plot 530b are scaled to the 125 sq-m measured area plot 530c. That is, the 125 sq-m measured area plot 530c is the reference relationship between the observed precipitation rate values ​​and the expected maximum precipitation rate values. The 125 sq-m measured area plot 530c assumes that the observed precipitation rate values ​​are instantaneous precipitation rate values. Thus, there is a one-to-one relationship between the observed precipitation rate values ​​and the expected precipitation rate values. Thus, the 125 sq-m measured area plot 530c may be considered the highest spatial resolution measured area, Therefore, there is no maximum precipitation rate within the measurement area of ​​125 sq. m.

[0071] As described above, the plot 530a of the 16 sq-km measured area is scaled relative to the plot 530c of the 125 sq-km measured area. Thus, in contrast to the plot 530c of the 125 sq-km measured area, the plot 530a of the 16 sq-km measured area shows that the maximum precipitation rate likely occurred when averaged over the 16 sq-km measured area. According to the plot 530a of the 16 sq-km measured area, the expected maximum precipitation rate value is greater than the observed precipitation rate value determined for the 16 sq-km measured area. For example, if the observed precipitation rate averaged over the 16 sq-km measured area is 20 mm / hr, then the plot 530a of the 16 sq-km measured area shows that the 125 sq-m measured area within the 16 sq-km measurement has a maximum precipitation rate of 36 mm / hr. Time scaling may also be performed, as described below.

[0072] FIG. 6 illustrates a chart 600 showing a time scale adjustment from a relatively long measurement integration time to a 10 second sensor measurement integration time. As shown in FIG. 6, the chart 600 includes an observed precipitation rate axis 610 and a predicted precipitation rate axis 620, which are in units of mm / hour, although any suitable units may be used. The chart 600 also includes a measurement integration time relationship 630. The measurement integration time relationship 630 relates the observed precipitation rate values ​​calculated at the observed precipitation rate measurement integration time to the predicted precipitation rate calculated at the predicted precipitation rate interval. The measurement integration time relationship 630 consists of a plot 630a of a 1 hour measurement integration time, a plot 630b of a 10 minute measurement integration time, and a plot 630c of a 10 second measurement integration time.

[0073] The plot 630a of the 1 hour measurement integration time and the plot 630b of the 10 minute measurement integration time are scaled to the plot 630c of the 10 second measurement integration time. That is, the plot 630c of the 10 second measurement integration time is the reference relationship between the observed precipitation rate value and the expected precipitation rate value. The plot 630c of the 10 second measurement integration time assumes that the observed precipitation rate value is the instantaneous precipitation rate value. Thus, there is a one-to-one relationship between the observed precipitation rate value and the expected precipitation rate value. Therefore, the plot 630c of the 10 second measurement integration time may be considered as the highest time resolution measurement integration time, and therefore, there can be no maximum precipitation rate within the 10 second measurement integration time.

[0074] As discussed above, the plot 630b for the 10 minute measurement integration time is scaled relative to the plot 630c for the 10 second measurement integration time. Thus, in contrast to the plot 630c for the 10 second measurement integration time, the plot 630b for the 10 minute measurement integration time shows that the maximum instantaneous precipitation rate likely occurred during the 10 minute measurement integration time. According to the plot 630b for the 10 minute measurement integration time, the predicted maximum instantaneous precipitation rate value is greater than the observed precipitation rate value determined using the 10 minute measurement integration time.

[0075] For example, suppose a rain gauge measures precipitation rate by averaging (accumulating) over 10 minutes, and the rain gauge reports a precipitation rate of 30 mm / hour. The actual instantaneous precipitation rate may vary during the 10 minute measurement integration time, and thus the maximum instantaneous precipitation rate may occur during the 10 minute measurement integration time. The measurement integration time relationship 630 may be used to scale an observed precipitation rate value based on the 10 minute measurement integration time to an expected maximum instantaneous precipitation rate value during the 10 minute measurement integration time. More specifically, the 10 minute measurement integration time plot 630b may be used to determine that a maximum precipitation rate of approximately 36 mm / hour based on a 10 second measurement integration time likely occurred within the 10 minute measurement integration time of the observed integrated precipitation rate value of 30 mm / hour.

[0076] 7 illustrates a chart 700 showing the probability distribution of a climatological metric relationship between maximum and average precipitation rates. As shown in FIG. 7, the chart 700 ranges from 0 to the climatological maximum. A bin axis 710 is shown with bins ranging from Cmax to Cmax, although any suitable range may be employed. Chart 700 also includes a relative occurrence climate characteristic axis 720, which is a percentage of the total number of occurrences. Also shown is a cumulative percentage axis 730 ranging from 0 to 100 percent, which represents the accumulation of the probability distribution 740 from 0 to 100 percent. Chart 700 also includes a probability distribution 740 with multiple bins and a cumulative probability distribution 750.

[0077] The probability distribution 740 may represent an area or region, such as the region of interest 320 described above. That is, the probability distribution 740 may be the climatological precipitation rates for the area 310a of the region 310 scaled in time and / or space to the region of interest 320, as described above with reference to Figures 4-6. Alternatively, the probability distribution 740 may represent the region 310 described above, where the temporal and / or spatial scale of the exceedance rate thresholds are scaled to the temporal and / or spatial scale of the area 310a of the region 310. That is, in this latter example, the order of the spatial scale adjustment 440, the time scale adjustment 450, and the sampling adjustment 460 may be reversed, with the probability distribution 740 being used for the sampling adjustment 460 before the time scaling adjustment 450 and / or the spatial scaling adjustment 440.

[0078] The width of a given bin in the probability distribution 740 is the range of the logarithms of the maximum and mean values ​​of the average precipitation rate of the region. For example, if the maximum and mean values ​​of the average precipitation rate of the region are equal to each other, the ratio is 1, which has a log value of zero for the first bin of the probability distribution 740. The bin axis 710 is unitless. Thus, the chart 700 includes a count axis 720 whose log(max / mean) value counts the number of occurrences that occur within the width of the given bin. The count axis 720 is unitless. A cumulative probability distribution 750 of the probability distribution 740 is also shown.

[0079] 7 characterizes climatological metrics of a climatological sample, such as the precipitation data 410 described above, using a climatological metric relationship of log(max / mean), for example, as determined from the year of data in a measurement area. Operationally, the mean value of log(max / mean) may be the average value of 16 measurement areas within a region, such as region 310 discussed above. The maximum value of log(max / mean) may be the maximum precipitation rate for the measurement area.

[0080] The probability that the precipitation rate may be greater than the exceedance rate threshold may be calculated by determining the logarithm of the ratio between the exceedance rate threshold and the average value of the zone or region. For example, the exceedance rate threshold may be the maximum precipitation rate of the sensor. The maximum precipitation rate may be divided by the average value of the precipitation rate of the region. For example, the logarithm may be calculated by the ratio of the maximum precipitation rate of the sensor to the average value of the precipitation rate for the zone or region, such as the region of interest 320 described above. The result of this calculation may be compared to the cumulative probability distribution 750 to determine the probability that the precipitation rate may exceed the maximum precipitation rate of the sensor in the zone or region.

[0081] In an exemplary calculation using Figures 5-7, a precipitation rate may be measured for an area, such as section 310a of region 310 described above. As shown in Figure 3, region 310 may have an area of ​​16 sq-km. The precipitation rate may be measured over an integral or accumulated measurement period of, for example, 10 minutes. An average value for section 310a may be calculated as described above.

[0082] An exceedance rate threshold may also be determined, which may be a maximum precipitation rate for a sensor, such as the LIDAR 212l described above. A vehicle, such as the vehicle 140 described above, may determine and provide the exceedance rate threshold. The maximum precipitation rate for the LIDAR 212l may be the rate at which the LIDAR 212l does not operate properly. The maximum precipitation rate for a sensor may be considered an instantaneous rate, such as 60 mm / hour, for a relatively small area. The area may be, for example, 100 sq-m.

[0083] The exceedance rate thresholds may be time-scaled to reflect the expected maximum precipitation rate taken over a 10 minute interval. For example, chart 600 shows a 6 minute average over a 10 second measurement integration time. The chart 500 may be used to adjust the 0 mm / hour to 50 mm / hour for a 10 minute measurement integration time. The exceedance rate threshold may also be spatially adjusted to reflect the expected maximum precipitation rate for a measurement area of ​​16 sq-km. For example, the chart 500 may be used to spatially adjust a time-adjusted expected precipitation rate of 50 mm / hour for a 125 sq-m area to a expected maximum precipitation rate of 27 mm / hour for a 16 sq-km area.

[0084] Chart 700 may be used to perform a sampling adjustment to the 27 mm / hr temporally and spatially scaled exceedance rate threshold to determine the probability that the actual precipitation rate in the region of interest 320 may exceed the 60 mm / hr exceedance rate threshold. For example, the logarithm of the 27 mm / hr temporally and spatially scaled exceedance rate threshold divided by the mean of the averaged precipitation rates for sections 310a of region 310 may be calculated and compared to chart 700, particularly bin axis 710. The probability of that precipitation rate for region of interest 320 may be calculated from cumulative probability distribution 750.

[0085] As discussed above with reference to Figures 5-7, spatial and temporal scaling may be performed before sampling adjustment is performed. For example, in contrast to Figure 4, sampling adjustment may be performed before spatial and temporal adjustment, for example, by providing chart 700 using climatological precipitation rates that are adjusted to the spatial and temporal scales of the exceedance rate thresholds. It will also be appreciated that probabilities may be provided rather than exceedance rate thresholds. For example, the vehicle may provide one or more probabilities, such as a list of probabilities. The one or more probabilities may correspond to desired operational up times of different sensors of the vehicle. The one or more probabilities may be provided and used to calculate corresponding exceedance rate thresholds, which may be calculated prior to the temporal and / or spatial scaling.

[0086] These and other calculations may be performed by any suitable method performed by a station, processor, etc., such as the above-mentioned weather information station 120 and / or processor 130. An exemplary method 800 is described below.

[0087] FIG. 8 illustrates a method 800 for scaling and statistical adjustment of precipitation rates for automated driving. Method 800 may be performed by weather information station 120 and / or processor 130 described above, although any suitable weather information station and / or processor may be used. Thus, processor 130 may be configured to perform the following steps. As shown in FIG. 8, method 800 obtains precipitation rates for each section of a region in step 810. In step 820, method 800 determines a climatological metric relationship between at least two climatological metrics of precipitation rates. In step 830, method 800 determines an operational metric relationship between at least two operational metrics of precipitation rates. Method 800 compares the climatological metric relationship to the operational metric relationship in step 840. The comparison may determine a probability associated with a predetermined exceedance rate threshold and / or an exceedance rate threshold associated with a predetermined probability in step 850.

[0088] The climatological metrics may be maximum and average values ​​of climatological samples of precipitation rates for sections of the region. The operational metrics may be maximum and average values ​​of operational samples of precipitation rates for sections of the region. The climatological samples may be created over multiple years and / or seasons and therefore may have a statistically large sample size. That is, the climatological samples may result in statistically significant climatological statistical metrics. The operational samples may be precipitation rates that are close in time and / or space to the vehicle, city, etc. For example, the operational samples may consist of instantaneous precipitation rates for the region that the vehicle may be experiencing and / or is expected to experience. For example, the vehicle may be traversing a region currently experiencing a weather system with a precipitation rate of 60-100 mm / hour. Thus, an operational sample may include the precipitation rate of a weather system, but may not include the precipitation rate of a historical weather system.

[0089] The climatological metric relationships may include relationships between maximum and average values ​​of climatological samples of precipitation rates for sections of a region. The operational metric relationships may include relationships between maximum and average values ​​of operational samples of precipitation rates for sections of a region. The climatological metric relationships and operational metric relationships may be ratios, differences, etc., and / or values ​​derived from ratios, differences, etc. For example, referring to FIG. 7, the climatological metric relationships and operational metric relationships may be the logarithms of maximum and average values ​​of precipitation rates for a section of a region.

[0090] The probability associated with a given exceedance rate may be the probability that the maximum precipitation rate for one or more sections of the region is greater than the exceedance rate threshold. For example, method 800 may determine the probability that the precipitation rate may exceed 100 mm / hr using flowchart 400, which may be executed by weather information station 120 and / or processor 130, for example. In one example, method 800 may determine the probability by referencing cumulative probability distribution 750 shown in FIG. 7 using the logarithm of the ratio of the maximum precipitation rate to the average precipitation rate for the section.

[0091] The method 800 may further include performing a spatial scale adjustment of the precipitation rate of the area of ​​the region to the spatial scale of the operational region. For example, as described above with reference to FIG. 5, a precipitation rate taken over a large area, such as 16 sq-km, may be scaled to 100 sq-m. 100 sq-m may be an operational scale and therefore useful for automated driving. That is, a sensor in, for example, the vehicle 140 described above, may have a maximum precipitation rate that is considered an instantaneous precipitation rate, and therefore any precipitation rate within 100 sq-m that is greater than the maximum precipitation rate of the sensor will result in degradation of the sensor's performance.

[0092] The method 800 may further include performing a time scale adjustment of the precipitation rate of the area of ​​the region to the time scale of the operational period. For example, as described above with reference to FIG. 6, a precipitation rate taken with a measurement integration time of 10 minutes may be scaled to a measurement integration time of 10 seconds. The measurement integration time of 10 seconds may be an operational scale and therefore more useful for automated driving. That is, a sensor in, for example, the vehicle 140 described above, may have a maximum precipitation rate that is considered an instantaneous precipitation rate, and therefore any precipitation rate during the measurement integration time of 10 minutes that is greater than the maximum precipitation rate of the sensor will result in degradation of the sensor's performance.

[0093] The exceedance rate threshold may be based on a maximum precipitation rate for the sensor. For example, the exceedance rate threshold may be a maximum precipitation rate for the sensor. As discussed above, the maximum precipitation rate for the sensor may be a precipitation rate at which the performance of the sensor degrades. The sensor may be the sensor 212 described above, although any suitable sensor may be employed.

[0094] The system 100 and method 800 for precipitation rate scaling and statistical adjustment for automated driving may provide a probability that a precipitation rate for an area of ​​interest, such as an area around a vehicle, will exceed a sensor's maximum precipitation rate. For example, the vehicle may provide a sensor's maximum precipitation rate and receive a probability that the precipitation rate will exceed an exceedance rate threshold. The probability may be provided to the vehicle by a weather information system that receives the maximum precipitation rate. Additionally or alternatively, the system 100 and method 800 may provide an exceedance rate threshold once the probability is provided. For example, the vehicle may provide the probability for an algorithm to adjust based on the probability that the precipitation rate will exceed the sensor's maximum precipitation rate by a certain amount.

[0095] Therefore, the vehicle may adjust the input to the algorithm to rely more or only on the data provided by the sensor having the higher maximum precipitation rate, Adjustments may be made, such as slowing down the vehicle or returning control of the vehicle to the vehicle operator. Such adjustments may be made even though the time and / or spatial scales of the precipitation rate measurements may not be similar to the time and / or spatial scales of the sensors. Thus, conventional precipitation rate measurements may be utilized for automated driving without significant investment in precipitation rate measurement infrastructure.

[0096] The detailed description of the above embodiments is not an exhaustive description of all embodiments contemplated by the inventors within the scope of the present description. Indeed, those skilled in the art will recognize that certain elements of the above embodiments may be combined or removed in various ways to create further embodiments, and such further embodiments are within the scope and teachings of the present description. It will also be apparent to those skilled in the art that all or part of the above embodiments may be combined to create further embodiments within the scope and teachings of the present description.

[0097] Thus, while specific embodiments are described herein for illustrative purposes, various equivalent modifications are possible within the scope of this description, as those skilled in the art will recognize. The teachings provided herein may be applied to other systems and methods that use weather data to improve weather information, as well as the embodiments described above and illustrated in the accompanying drawings. Thus, the scope of the above embodiments should be determined from the claims that follow.

Claims

1. A system (100) for scaling and statistical adjustment of precipitation rates for a device having a sensor that is affected by precipitation, comprising: a meteorological information station (120) configured to provide precipitation rates for an area of ​​the region; a processor (130) in communication with the weather information station (120), the processor (130) comprising: obtaining the precipitation rate for the area of ​​the region; determining a climatological metric relationship between at least two climatological metrics of the precipitation rate; determining an operational metric relationship between at least two operational metrics of the precipitation rate; comparing the climatological metric relationships to the operational metric relationships; the probability associated with a given exceedance threshold and and an exceedance rate threshold associated with a predetermined probability.

2. the at least two climatological metrics include a maximum and a mean of the precipitation rate climatological samples for the area of ​​the region; The system (100) of claim 1, wherein the at least two operational metrics include a maximum and a mean of operational samples of the precipitation rate for the section of the region.

3. the climatological metric relationship includes a relationship between the maximum and the average of the climatological samples of the precipitation rate for the area of ​​the region; The system (100) of claim 2, wherein the operational metric relationship comprises a relationship between the maximum and the average of the operational samples of the precipitation rate for the section of the region.

4. 2. The system of claim 1, wherein the probability associated with the predetermined exceedance rate threshold and the predetermined probability is a probability that a maximum precipitation rate in one or more sections of the region is greater than the predetermined exceedance rate threshold.

5. The system of claim 1 , wherein the processor is further configured to perform a spatial scale adjustment of the precipitation rates for the areas of an operational domain to a spatial scale of the operational domain.

6. The system of claim 1 , wherein the processor is further configured to perform a time scale adjustment of the precipitation rates for the areas of the domain to a time scale of an operational period.

7. The system (100) of claim 1, wherein the exceedance rate threshold is based on a maximum precipitation rate for a sensor within the vehicle.

8. 1. A method for precipitation rate scaling and statistical adjustment for an apparatus having a sensor that is subject to precipitation, comprising: Obtaining a precipitation rate for an area of ​​a region; determining a climatological metric relationship between at least two climatological metrics of the precipitation rate; determining an operational metric relationship between at least two operational metrics of the precipitation rate; comparing the climatological metric relationships to the operational metric relationships; the probability associated with a given exceedance threshold and and determining at least one of an exceedance rate threshold associated with the predetermined probability.

9. the at least two climatological metrics include a maximum and a mean of the precipitation rate climatological samples for the area of ​​the region; The method of claim 8 , wherein the at least two operational metrics include a maximum and a mean of operational samples of the precipitation rate for the section of the region.

10. the climatological metric relationship includes a relationship between the maximum and the average of the climatological samples of the precipitation rate for the area of ​​the region; The method of claim 9 , wherein the operational metric relationship comprises a relationship between the maximum and the average of the operational samples of the precipitation rate for the section of the region.

11. 9. The method of claim 8, wherein the probability associated with a predetermined exceedance rate threshold and the predetermined probability is the probability that a maximum precipitation rate in one or more sections of the region is greater than the predetermined exceedance rate threshold.

12. The method of claim 8 , further comprising performing a spatial scale adjustment of the precipitation rates of the sections of an operational domain to a spatial scale of the domain.

13. The method of claim 8 , further comprising performing a time scale adjustment of the precipitation rates for the areas of the domain to a time scale of an operational period.

14. The method of claim 8 , wherein the exceedance rate threshold is based on a maximum precipitation rate for a sensor within the vehicle.

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