40 results about "Sky imaging" patented technology

The invention discloses a solar irradiance prediction system and method based on dual attention multi-modal fusion. The method comprises the following steps: performing spatial feature extraction on a sky imaging observation map after preprocessing and time synchronization by using a MobileNetV2 network to obtain a sky imaging time sequence feature sequence; inputting the sky imaging time sequence feature sequence into a DAT model to obtain a global sky imaging time sequence feature sequence, a local sky imaging time sequence feature sequence and a local convolution position coding residual term; obtaining an aggregated sky imaging time sequence feature sequence according to the global sky imaging time sequence feature sequence, the local sky imaging time sequence feature sequence and the local convolution position coding residual item; performing time-dependent modeling on the meteorological observation data by adopting a long short-term memory network to obtain a meteorological observation data feature sequence; and performing feature fusion on the aggregated sky imaging time sequence feature sequence and the meteorological observation data feature sequence through a trans-attention mechanism to obtain a fused joint feature sequence for solar irradiance prediction.

The invention relates to the technical field of photovoltaic power prediction, in particular to an ultra-short-term prediction system and method based on image analysis, and the system comprises an all-sky imaging data processing module, a cloud layer dynamic feature calculation module, a solar irradiance change prediction module, a photovoltaic power generation power calculation module and a prediction model optimization training module. According to the method, the cloud layer thickness, transparency, movement direction and coverage rate change are calculated, the cloud layer dynamic characteristic analysis precision is enhanced, and the radiation scattering coefficient is calculated in combination with the direct solar light shielding ratio, the cloud layer characteristics and the solar angle, so that the irradiance calculation is more accurate, the illumination change rate is analyzed, and the short-term irradiance trend is dynamically adjusted; the total power is calculated and the shielding influence is adjusted in combination with component arrangement, so that the power prediction is more practical, the input data is normalized, the time sequence sample is enhanced, the dynamic adjustment of the model parameters is optimized, and the adaptive capacity and the prediction stability are improved.

The present invention discloses a full-field refined DNI prediction method. At least two total-sky imagers are used to determine the actual position of a cloud, and then a shadow position is determined on the basis of a solar angle; and the thickness of the cloud is determined by means of the imaging brightness of the cloud, and then a DNI value is predicted. The method specifically comprises the following steps: performing cloud identification, cloud image speed calculation, cloud actual-position calculation, cloud / shadow actual-speed calculation, shadow position prediction, cloud thickness extraction, DNI mapping, and DNI prediction. In the method, at least two total-sky imagers or pinhole cameras are used to perform a DNI prediction operation, and a DNI change at each specific position in a heliostat field can be accurately predicted, such that the power generation efficiency is improved.

The invention provides a multi-climate zone-oriented photovoltaic car shed assembly inclination angle adjusting method and system, and the method comprises the steps: obtaining a low-altitude topographic cumulus real-time distribution image and sky brightness data through an all-sky imager, and combining the speed and direction of valley wind collected by an anemorumbometer, generating a meteorological feature data set including a cloud body edge, a shadow boundary and a moving path; extracting a shadow coverage state of the high-altitude shed from the shadow evolution time sequence, if the high-altitude shed is not in shadow coverage, determining that a solar panel of the high-altitude shed maintains a target direct light receiving inclination angle, and generating an inclination angle maintaining instruction; the inclination angle adjusting instruction and the inclination angle maintaining instruction are distributed to solar panel control units of low-altitude and high-altitude carports, servo motors are driven to adjust panel angles, and inclination angle optimization is completed; and evaluating the data set according to the optimization effect, calculating the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency, and updating the terrain constraint parameters according to the deviation.

The invention relates to the field of weather forecast, and discloses a DNI prediction method based on all-sky imaging and clear sky DNI fitting, which comprises the following steps: screening clear sky DNI data from DNI historical data, fitting a clear sky DNI model, and then carrying out clear sky DNI calculation by using the clear sky DNI model obtained by fitting to obtain clear sky DNI at each time point; inputting an all-sky image obtained in real time and an all-sky image obtained in the previous one minute into the trained double-picture convolutional neural network model, and predicting to obtain a clear sky index after # imgabs0 # minute; and multiplying the predicted clear sky index of the # imgabs 1 # after the minute by the corresponding clear sky DNI to obtain a predicted DNI value of the # imgabs 2 # after the minute. The method disclosed by the invention can reflect the change of actual DNI more accurately, has better prediction accuracy and strong adaptability, can be used for tower type solar thermal power generation systems in different regions, and is beneficial to reducing the operation cost of a photo-thermal power station.

The invention provides a temperature and humidity sensor external device of an all-sky imager, and is applied to the technical field of temperature and humidity sensors. The device comprises an imager body, a mounting ring is mounted on the edge of the top of the imager body, a gear ring is connected to the outer wall of the mounting ring, a mounting seat is slidably mounted on the mounting ring, a mounting cover is mounted on the mounting seat, and a sun shade is connected to the outer wall of the mounting cover. And the revolution assembly is used for driving the sensor probe to revolve by taking the imager main body as the center. Through the arrangement of the sun shade, the sun shade can prevent sunlight from directly irradiating the sensor probe to influence the temperature measurement accuracy; through the arrangement of the revolution assembly, the mounting seat can rotate on the mounting ring by utilizing wind power, so that the sensor probe on the mounting seat continuously moves, the sensor probe is uniformly contacted with air around the imager main body, and the accuracy of temperature and humidity measurement is improved.

The invention provides a photoelectric solar irradiance meter self-calibration device for outdoor monitoring and a calibration method, and belongs to the technical field of photovoltaic monitoring. The device comprises a sky imager, a solar irradiance meter for monitoring, a standard solar irradiance meter, a sealed darkroom, a three-dimensional adjusting bracket and a waterproof sealed box, wherein a dual-channel temperature collector, a dual-channel voltage collector and a control, data processing and storage module are integrated in the waterproof sealed box. The sealed darkroom normally protects the standard irradiance meter to prevent the standard irradiance meter from aging and dust deposition; the dual-channel collector synchronously collects temperature and voltage of a standard solar irradiance meter for monitoring, and supports a calibration algorithm to realize dynamic updating of sensitivity; the three-dimensional adjusting bracket ensures that the device is parallel to the mounting plane of the photovoltaic module, and the angle response matching degree is improved; the sky imager judges the calibration weather condition to guarantee the effectiveness of the calibration environment; the control module realizes automation of a self-calibration process, adapts to an outdoor long-term monitoring scene, and realizes continuous and accurate measurement of photovoltaic solar irradiance.

1. Name of the product in this design: Intelligent All-Sky Imager. 2. Purpose of this design: This design is used for all-weather intelligent observation of cloud cover and cloud base, and directly outputs cloud cover information and cloud height information. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The present application relates to the technical field of photovoltaic power generation, and particularly relates to an ultra-short-term photovoltaic power prediction method, system, device and medium. The present application fuses two types of heterogeneous observation data of all-sky imagers and geostationary satellites, respectively calculates cloud transmittance time series of different historical windows, utilizes the real-time and non-lagged characteristics of all-sky imagers to capture minute-level cloud changes, and utilizes the wide-range observation capability of geostationary satellites to obtain the macro trend of cloud movement, thereby effectively making up for the deficiencies of a single data source in timeliness and spatial coverage. On this basis, a pre-trained time series prediction model is used to estimate the cloud transmittance of a future target period, and is further converted into ground surface irradiance and photovoltaic power, which can significantly improve the accuracy and stability of ultra-short-term power prediction, and provide more reliable technical support for power grid dispatching and photovoltaic power station operation.

This invention relates to the field of cloud tracking technology, specifically to a cloud tracking visual recognition photovoltaic power monitoring device, comprising an all-sky imager, a protective component, a blocking component, and a cooling component on the outer side of the all-sky imager; the protective component includes a protective shell, and a fixing ring is provided inside the protective shell, with a gear ring rotatably embedded inside the fixing ring, and a protective lens fixedly connected to the outer surface of the gear ring, the protective lens being a spherical fisheye composite beam splitter; the purpose of this invention is to solve the problem that existing all-sky imagers, during long-term outdoor operation, have their core imaging lenses directly exposed to complex external environments, lacking a stable and long-lasting physical isolation protection system, and the lens surface is easily contaminated with various pollutants such as sand, salt spray, bird droppings, and insect carcasses.

The present invention relates to the field of weather forecasting and discloses a DNI prediction method based on full-sky imaging and clear-sky DNI fitting. The method comprises the following steps: filtering clear-sky DNI data from historical DNI data, fitting a clear-sky DNI model, and then using the fitted clear-sky DNI model to calculate the clear-sky DNI to obtain the clear-sky DNI at each time point; inputting the real-time full-sky imaging and the full-sky imaging of the preceding minute into a trained dual-image convolutional neural network model to predict the clear-sky index for the next minute; and multiplying the predicted clear-sky index for the next minute by the corresponding clear-sky DNI to obtain the predicted DNI value for the next minute. The method disclosed in the present invention can more accurately reflect actual DNI changes, has better prediction accuracy, and is highly adaptable. It can be used in tower-type solar thermal power generation systems in different regions, helping to reduce the operating costs of concentrated solar power plants.

The utility model discloses an integrated all-sky meteorological monitoring device, which comprises a box body, an ultrasonic meteorological instrument, an all-sky imager, a total solar radiation sensor, a power supply module and a communication module, and is characterized in that the ultrasonic meteorological instrument is arranged on one side of the top of the box body, and the all-sky imager is arranged on the other side of the top of the box body; a total solar radiation sensor is arranged along the horizontal direction of the top of the box body and the inclination direction of the front side of the box body respectively, the power supply module and the communication module are arranged in the box body, and the ultrasonic meteorological instrument, the all-sky imager and the total solar radiation sensors are electrically connected with the power supply module and the communication module respectively. The ultrasonic meteorological instrument, the all-sky imager, the total solar radiation sensor, the power supply module, the communication module and other components are integrated on the box body, the portability is high, the use is flexible, the field installation and debugging are convenient, and the installation and use efficiency is further improved.

The invention relates to the technical field of cloud cover recognition, and discloses a double-camera all-sky imager cloud cover recognition system, which comprises a visible light camera, an infrared camera, a graphics processor connected with the visible light camera and the infrared camera, and a deep learning framework connected with the graphics processor, according to the system, an SSTD-Unet cloud cover segmentation network is adopted to train independent models for visible light and infrared images, and the SSTD-Unet cloud cover segmentation network is of a symmetrical structure composed of a down-sampling coding part, a bottleneck part and an up-sampling decoding part. The cloud cover identification system can realize stable and high-precision cloud cover identification under various weather and illumination conditions, and compared with a traditional threshold segmentation method, the cloud cover identification system has the advantages that the performance is obviously improved, and a reliable solution is provided for realizing high-precision, full-automatic and all-weather cloud cover identification.

The invention provides a carbon flux metering inversion method and system for a Saggohm new energy base, and the method comprises the steps: obtaining the physical parameters, topographic data and image data of the Saggohm new energy base, and building a three-dimensional geometric model through a three-dimensional reconstruction technology; performing space grid processing on the model area to generate an LBM computational grid; inputting the image data into the trained ground parameter model, and outputting a ground boundary parameter and a ground source item parameter of each grid unit; cloud layer information is monitored through an all-sky imager, and ground irradiation intensity distribution is calculated by adopting a Monte Carlo method in combination with a sun trajectory tracking model; acquiring an atmosphere input boundary parameter and a source item parameter; solving the LBM model to obtain carbon dioxide concentration, flow velocity, temperature and pollutant concentration distribution; and the carbon flux of the base is calculated based on the simulation result, so that high-precision, low-cost and spatially continuous inversion calculation of the large-range carbon flux of the Sagomean region is realized.

The invention belongs to the technical field of cloud measurement, and particularly relates to a cloud region identification and cloud amount calculation method and system based on an all-sky imager, a normalized difference processing mode is selected to identify a cloud region, and a brightness threshold is introduced for a sky image which is strong in sun illumination and is not completely covered by a cloud layer; selecting an initial threshold value based on a maximum between-class variance method, and selecting a brightness threshold value for the sky image into which the brightness threshold value is introduced by using the maximum between-class variance method; performing cloud region preliminary identification on the whole-sky image through the initial threshold value, and adjusting the threshold value according to image features to realize cloud region identification; performing morphological smoothing processing on the sky image after the cloud region identification; geometric correction is carried out on the image after smoothing processing, and the cloud cover is calculated; the method has the advantages that the sky image is processed through differentiation, the brightness threshold value is introduced into the strong light interference cloud region, the noise influence is reduced by combining morphological smoothing processing, the cloud region recognition precision is effectively improved, and the cloud amount calculation error is finally reduced through geometric correction.

The application relates to the field of DNI prediction, and discloses a DNI prediction method based on a dense optical flow method and a deep learning network, which comprises the following steps: performing distortion correction on images collected by a full-sky imager; according to images at two time points before and after distortion correction, the speed of each pixel point of a current image is calculated by using a dense optical flow method; when the maximum pixel speed is less than a specified speed threshold, a static model is used to predict the DNI at a future time point, otherwise, a motion model is used to predict the DNI at the future time point; when the motion model is used, a trained deep learning network model is used to predict the clear sky index at the future time point, and then the clear sky index at the future time point is multiplied by the clear sky DNI at the future time point, so that the predicted DNI at the future time point can be obtained. The method disclosed by the application can more accurately reflect the change of the actual DNI, has better prediction accuracy, and is highly adaptable.

The present invention discloses a multi-modal fusion photovoltaic power prediction method for all-sky imager data, comprising the following steps: obtaining the time-series sky image data in the all-sky imager and the historical photovoltaic power data of the photovoltaic array; respectively extracting features from the time-series sky image data and the historical photovoltaic power data to obtain the modal feature information of the time-series sky image data and the modal feature information of the historical photovoltaic power data; fusing the modal feature information of the time-series sky image data and the modal feature information of the historical photovoltaic power data to obtain a feature matrix; obtaining the final latent features according to the feature matrix; training a photovoltaic power prediction model using the final latent features; and predicting the photovoltaic power using the photovoltaic power prediction model. The present invention performs feature extraction processing on the time-series sky image data and the historical photovoltaic power data, effectively reducing the prediction error caused by the intermittency and uncertainty of solar power generation.

The present invention discloses a system and method for atmospheric observations. The system (100) comprises a sky observer (102) comprising a sky imager (104) to capture and communicate a sky image for analyzing cloud characteristics, a robotic sun photometer (106) to track and communicate the sun and measure aerosol properties, a pyranometer (108) with a shadowband (110) and a sensor unit (120) to measure environmental conditions and collect and communicate sensor data surrounding the sky observer (102). The system (100) comprises a communication network (114) communicatively connected to the sky observer (102) to collect aggregated atmospheric data comprising the sky image, aerosol properties, different components of solar irradiance and the sensor data and transmit the aggregated atmospheric data to a server (114). The server (114) receives and analyzes the aggregated atmospheric data to estimate impact of atmospheric conditions on solar energy production in a solar plant.

This invention relates to the field of solar energy control and discloses a solar energy intelligent control system based on brightness gradation extraction, comprising: a panoramic perception module for acquiring first image data of the entire sky field of view; a near-sun tracking sampling module including a dual-axis servo frame and a narrow-angle camera mounted thereon, wherein the narrow-angle camera uses a physical light-reducing component to suppress the pixel brightness of the central region of the sun to the unsaturated range, for acquiring second image data of the near-sun region; and an edge processing unit electrically connected to the panoramic perception module and the near-sun tracking sampling module, for executing brightness gradation logic based on the first and second image data and generating converter control commands, thus solving the overexposure problem of traditional all-sky imagers near the direct sunlight point.

The application discloses a kind of measurement method and measurement system of the spectral distribution of PAR, including by the geometric calibration and radiation calibration of all-sky imager, the observation zenith angle and observation azimuth of each pixel of all-sky image and RGB waveband radiance value are acquired;Through cloud detection algorithm, all-sky image is divided into clear sky and cloudy condition;For clear sky all-sky image, combined with radiation transfer model, PAR spectral radiation distribution algorithm reconstruction is carried out to obtain the spectral distribution of PAR;For the image with cloud, a deep learning model is used to obtain the spectral distribution of PAR, and finally the spectral distribution of the whole PAR is obtained.The application has the advantages that: the spectral distribution of PAR is reconstructed using all-sky image, and by combining physical radiation model and deep learning model, the spectral irradiance of visible light band reaching the ground, i.e.PAR spectral distribution information, is obtained from the image captured by all-sky imager.

The present invention discloses a full-sky cloud imaging system based on a neuromorphic visual sensor, comprising a full-sky imager, a sunshade belt, a data acquisition module, and a data calculation module; the data acquisition module comprises a hemispherical mirror and an event-driven sensor for acquiring sky-related data; the full-sky imager uses an event-driven sensor for shooting, the event-driven sensor being arranged on a support arm of the full-sky imager and located above the center of the hemispherical mirror; the sunshade belt covers the surface of the hemispherical mirror; and the data acquisition module is connected to the data calculation module via a transmission module. The present invention has higher temporal resolution and dynamic range, and can more accurately identify and track targets by monitoring the movement of objects in the image. It can also measure cloud cover and cloud speed, ultimately achieving full-sky cloud cover and cloud movement speed measurement and forecasting of the probability of clouds blocking the sun.

The invention discloses a regional cloud field collaborative prediction and correction method based on a multi-site sky imaging network, and the method comprises the following steps: constructing and deploying the multi-site sky imaging network, and selecting and deploying sparsely distributed network sites; collecting and preprocessing multi-source observation data; carrying out graph structure modeling and node feature extraction; space-time diagram neural network prediction: constructing a model integrating space-time convolution and a diagram neural network for prediction; performing multi-source observation data fusion and dynamic correction, fusing the multi-source observation data, and performing dynamic correction and virtual completion when the data is abnormal or missing; intelligently generating a missing cloud picture by adopting an adversarial network; and outputting a regional cloud field prediction result and early warning information, outputting a future short-time scale regional cloud field prediction result, and automatically generating the early warning information. According to the method, the authenticity and accuracy of regional cloud field collaborative prediction can be improved, so that support is provided for scheduling decision-making of wind and light power generation, and the problem that the scheduling capability of a wind and light power generation station cannot be well improved by using a cloud picture prediction technology at present is solved.

The invention relates to the technical field of all-sky imagers, in particular to an all-sky imager of a low-maintenance weather station. Comprising a supporting rod, the surface of the supporting rod is sleeved with a movable sleeve capable of vertically moving along the supporting rod, a supporting block is fixedly installed on the movable sleeve, the end of the supporting block is fixedly connected with a straight arm, one end of the straight arm is sleeved with an insertion sleeve fixedly connected with an all-sky imager body, two clamping grooves are formed in the inner wall of the insertion sleeve, and two clamping heads are slidably connected into the straight arm; the two clamping heads are inserted into the two clamping grooves of the inserting sleeve respectively, the servo motor drives the lead screw and the screw sleeve transmission assembly to drive the movable sleeve to vertically move along the supporting rod, and then lifting of the all-sky imager body is achieved. According to the scheme, the imager can be lowered to a safe height through remote control or ground operation, potential safety hazards such as high-altitude falling and electric shock are avoided, and meanwhile the labor cost of maintenance is reduced.

The present invention relates to a cloud position and cloud speed measurement method, comprising the following steps: obtaining the full sky image taken by each full sky imager and the coordinate point relationship between the pixel coordinates and the spatial coordinates in the three-dimensional spatial coordinate system; identifying the spatial feature matching points of the full sky images taken by the paired full sky imagers at the same time; calculating the azimuth of the connecting line between the two points in each spatial feature point pair; calculating the elevation and azimuth of the cloud feature point; calculating the spatial coordinates of the cloud feature point; calculating the correlation between the pixel coordinate change and the spatial coordinate change of the same full sky imager at two adjacent moments; and calculating the cloud movement speed. In the technical solution of the present invention, the network observation of ground-based cloud maps is utilized, and on the basis of the three-dimensional spatial positioning of the feature points on the cloud cluster in the cloud map, the time series of the full sky cloud map is analyzed and modeled to achieve quantitative observation of the cloud movement speed, thereby providing more accurate data support for cloud movement prediction and new energy applications.

The utility model provides an aerosol laser radar standard output platform deck, comprising a base, the upper part of which is used for fixing a radar acquisition end; the at least three supporting legs are hinged to the bottom of the base and used for supporting the ground; the base is sleeved with the rotating end, and the rotating end is rotationally connected with the base with the base as the axis; one end of the auxiliary supporting table is fixedly connected with the rotating end, and the other end of the auxiliary supporting table extends upwards, is located on one side of the radar acquisition end and is used for fixing a sky imager; the driving end is fixed below the base and used for driving the auxiliary supporting table to be rotationally connected around the base. Through the technical characteristics, the platform deck provides the radar acquisition end and the fixed end of the sky imager, the position of the rotating end is adjusted through the driving end to avoid the interference area of the radar acquisition end, the laying efficiency of the platform deck is improved, and normal work of related equipment is not affected.

The invention is suitable for the technical field of solar power generation, and provides a control method and device for a photovoltaic system, and the method comprises the following steps: obtaining the state information of a photovoltaic panel and the environment data of a place where the photovoltaic panel is located in real time; based on an illumination intensity prediction model, obtaining a maximum illumination intensity prediction value according to all-sky imaging time sequence data, sun position information and meteorological data; according to the maximum illumination intensity prediction value, the sun position information and the state information of the photovoltaic panel, an objective function about the photovoltaic power generation amount prediction value and mechanical energy consumption required by angle adjustment of the photovoltaic panel is established; according to the objective function, predicting to obtain a theoretical optimal angle adjustment value of the photovoltaic panel; and controlling the photovoltaic panel to perform angle adjustment according to the theoretical optimal angle adjustment value of the photovoltaic panel. According to the invention, through fusion of multi-source information, an illumination intensity change trend and a cloud layer movement track can be anticipated prospectively, so that the photovoltaic panel can be adjusted to an optimal position in advance, and the method has weather adaptability and dynamic prediction capability.

1. The name of the design product: sky imager. 2. The use of the design product: used for capturing the image of the sky clearly under the sunlight, analyzing the cloud amount, cloud shape and other parameters in the sky image. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: perspective view 1.

The invention discloses a photovoltaic prediction method and device based on sky image enhancement, equipment and a medium, and relates to the technical field of photovoltaic power generation. The method comprises the following steps: acquiring a real sky image acquired by all-sky imaging equipment installed near a photovoltaic power station; based on the real sky image, training a pre-constructed variational auto-encoder generation model, and performing image generation operation by using the trained variational auto-encoder generation model to obtain a generated new sky image; constructing a training sample of a to-be-trained photovoltaic prediction model according to the new sky image and the real sky image; and training a to-be-trained photovoltaic prediction model based on the training sample, and performing photovoltaic prediction by using the trained photovoltaic prediction model. According to the invention, a model is generated by using a trained variational auto-encoder, and a real sky image is expanded and enhanced to obtain a diversified sky image; and the enhanced sky image is used for photovoltaic prediction, so that the accuracy of photovoltaic prediction can be ensured.

The utility model provides an all-sky imager based on DVS identification. The all-sky imager comprises a telecentric fisheye lens, an inter-lens shutter, a filter lens, a 50% light splitting lens, an RGB imaging assembly and a DVS imaging assembly. The telecentric fisheye lens is installed at the foremost end of the imager, the inter-lens shutter is installed behind the telecentric fisheye lens, the filter lens is installed behind the inter-lens shutter, the 50% light splitting lens is installed behind the filter lens and used for splitting light, one part of the light after light splitting enters the RGB imaging assembly, and the other part of the light after light splitting enters the DVS imaging assembly. The all-sky imager can solve the problem that the existing all-sky imager cannot meet the data requirement of finer scale.

This application belongs to the field of cloud measurement technology, specifically relating to a method and system for cloud area identification and cloud cover calculation based on an all-sky imager. The method selects a normalized difference processing method to identify cloud areas, and introduces a brightness threshold for sky images with strong sunlight that are not completely covered by clouds. An initial threshold is selected based on the maximum inter-class variance (MOV) method, and then the brightness threshold is further selected using MOV for the sky images with the introduced brightness threshold. Preliminary cloud area identification is performed on the all-sky image using the initial threshold, and then the threshold is adjusted based on image features to achieve cloud area identification. Morphological smoothing is applied to the sky image after cloud area identification. Geometric correction is then performed on the smoothed image to calculate cloud cover. The advantages of this invention are that it utilizes differentiated processing of sky images, introduces a brightness threshold for cloud areas with strong light interference, combines morphological smoothing to reduce noise impact, effectively improves the accuracy of cloud area identification, and finally reduces cloud cover calculation errors through geometric correction.