MJO-based extension period precipitation forecasting system

Abstract

The invention provides an MJO-based extended-period rainfall forecasting system, which belongs to the technical field of rainfall forecasting and comprises an extended-period forecasting unit, an automatic data acquisition unit, a data storage unit and a login unit, and the extended-period forecasting unit, the automatic data acquisition unit and the login unit are all connected with the data storage unit. And the sub-season prediction unit is used for predicting rainfall according to seasons and historical data and generating corresponding icons. According to the method, the effective forecasting timeliness is prolonged, the method is easier to capture in a climate mode, the phase is highly correlated with regional rainfall, the method can serve as a forecasting anchor point, multi-scale coupling can be achieved, the tropical weather scale is coupled with a medium-latitude weather system through MJO, the local rainstorm and regional persistent drought and flood trend can be analyzed at the same time, data assimilation and ensemble forecasting optimization can be achieved, and the method is suitable for large-scale popularization and application. And carrying out extreme event early warning potential and cross-regional climate linkage prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of precipitation forecasting, and in particular to an extended-range precipitation forecasting system based on MJO. Background Art

[0002] In recent years, unprecedented attention has been paid to climate prediction research both domestically and internationally. This research not only holds significant social and economic significance but also scientific value. It is currently a major international research hotspot and has become a priority area of scientific development for countries at the end of this century and the beginning of the next. Short-term climate prediction has a solid scientific foundation, and current short-term climate prediction systems based on this foundation have demonstrated considerable predictive skill. However, current short-term climate prediction systems, whether relying on empirical statistical methods, climate model predictions, or dynamic or statistical downscaling, suffer from a lack of regional specificity, short prediction times, low accuracy, slow computational speed, and, most importantly, a single prediction target and unstable prediction results.

[0003] The MJO, a quasi-periodic oscillation in the tropics lasting 30-60 days, is a key factor in extended-range forecasts by modulating the monsoon system, mid-latitude circulation, and extreme weather events. The MJO, along with the monsoon intraseasonal oscillation (MISO), synergistically influences the precipitation distribution of the East Asian summer monsoon. Existing weather forecast systems fail to accurately predict precipitation, necessitating the development of an MJO-based extended-range precipitation forecast system. Summary of the Invention

[0004] The purpose of the present invention is to provide an extended-range precipitation forecast system based on the MJO to solve the technical problem that existing precipitation forecast systems cannot consider the relevant factors that extend the impact of rainfall.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] An extended-range precipitation forecast system based on the MJO includes a sub-seasonal forecast unit, a monthly forecast unit, an extended-range forecast unit, an automatic data acquisition unit, a data storage unit, and a login unit. The sub-seasonal forecast unit, the monthly forecast unit, the extended-range forecast unit, the automatic data acquisition unit, and the login unit are all connected to the data storage unit. The sub-seasonal forecast unit is used to predict precipitation by quarter based on historical data and generate corresponding icons. The monthly forecast unit is used to predict precipitation by month based on historical data and generate corresponding icons. The extended-range forecast unit is used for MJO precipitation rate forecast, CFSv2 daily forecast, and MJO dynamic similarity forecast. The automatic data acquisition unit is used to automatically download CFSv2.0 model forecast data for the next 50 days at time 0 every day. The data storage unit is used to store and manage data. The login unit is used for customers to log in and set permissions.

[0007] Furthermore, the automatic data acquisition unit is used to download the MJO index of the NOAA Climate Prediction Center by season. The data period is from 1979 to the day of data download, and data is provided once every season. The downloaded data includes prate, temperature, wind speed and height field elements, and the downloaded data grid interval is 0.9375-1.0.

[0008] Furthermore, the data storage unit includes a download data storage module, a prediction data storage module and a user data storage module. The download data storage module is used to store the original data downloaded by the automatic data acquisition unit. The prediction data storage module is used to store the predicted icon data and perform data time marking. The label is stored according to the predicted time point. The user data storage module is used to store the user's login information and the user's personal information data.

[0009] Furthermore, the extended-range forecast unit includes an MJO precipitation rate forecast module, a CFSv2 daily forecast module and an MJO dynamic similarity forecast module. The MJO precipitation rate forecast module is used to forecast the MJO precipitation rate, and generates corresponding spatial maps and trend maps based on historical data to display data from similar years. The CFSv2 daily forecast module is used to generate corresponding climate field forecast charts based on specific locations and selected circulation fields. The MJO dynamic similarity forecast module is used to calculate and find similar years of historical MJO index, calculate the daily precipitation distribution in similar years, and use a synthetic method to obtain a precipitation forecast for the next 50 days.

[0010] Furthermore, the CFSv2 daily forecast module has set area option box, circulation field option box, start date, forecast time period, calculation type and graph type option box. When the forecast is completed and the corresponding icon is good, the user can choose to download it in txt or bin format.

[0011] Furthermore, the MJO dynamic similarity forecast module is equipped with regional option boxes, reporting start date and time period line options to generate a spatial map. It can also generate a trend map to display similar annual fee data and generate a bar chart. The height of the bar each day represents the amount of precipitation. The MJO dynamic similarity forecast module can also make site rainfall forecasts based on the set sites.

[0012] Furthermore, in the MJO dynamic similarity forecast module, click the MJO dynamic similarity menu to enter the forecast interface, set the parameters of the region, start date, forecast period, calculation type, and image type, click query to obtain the daily precipitation forecast for each station in the region or river basin. When switching to the daily precipitation forecast and verification of a single station, select a station in the drop-down box to automatically output the 45-day precipitation forecast result for the station. If it is a forecast backcalculation, the actual precipitation will be displayed at the same time.

[0013] Furthermore, in the MJO dynamic similarity forecast module, when forecasting precipitation trends for a certain period, select any period and click on the statistical chart on the interface. The system will automatically output the precipitation trend forecast results for that period. When outputting the precipitation process forecast test, switch to the precipitation trend test. The system will automatically output the average daily precipitation in the entire area during the selected forecast period.

[0014] Furthermore, in the sub-seasonal prediction unit, when outputting forecasts for years with high MJO correlation, select MJO correlation in the options, click on the distribution map, and the system will automatically output the precipitation synthesis results for years with high correlation. When outputting the 45-day daily precipitation forecast verification for a station, switch to the trend verification option, select a station, and the system will automatically output the 45-day precipitation trend and actual situation for that station.

[0015] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0016] The present invention extends the effective forecast period and is easier to capture in climate models. The phase is strongly correlated with regional precipitation and can be used as a forecast anchor point. It is capable of multi-scale coupling. The MJO couples the tropical weather scale with the mid-latitude weather system, and can simultaneously analyze local heavy rains and regional persistent drought and flood trends. It also optimizes data assimilation and ensemble forecasts, has the potential for early warning of extreme events, and cross-regional climate linkage predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a cross-sectional view of the system startup of the present invention;

[0018] Figure 2 It is the precipitation forecast map of each station on a daily basis of the present invention;

[0019] Figure 3 It is the extended period precipitation forecast map of the site of the present invention;

[0020] Figure 4 This is a forecast map of precipitation trends at each station in any future period similar to the MJO index of the present invention;

[0021] Figure 5 This is a precipitation forecast trend chart for Guangxi in the next 1-45 days according to an embodiment of the present invention;

[0022] Figure 6 This is the most relevant precipitation forecast map of the MJO index in the present invention;

[0023] Figure 7 This is the MJO index similarity precipitation forecast trend and actual situation map for the entire region;

[0024] Figure 8 It is the precipitation forecast map of any period similar to the MJO index of the present invention;

[0025] Figure 9This is the most relevant precipitation trend map for the entire region in the next 1-45 days for the MJO index of the present invention;

[0026] Figure 10 It is the CFSv2.0 daily forecast stamp map of the present invention;

[0027] Figure 11 This is an enlarged effect diagram of the CFSv2.0 forecast map of the present invention. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and by way of preferred embodiments. However, it should be noted that many of the details listed in this specification are merely provided to help the reader gain a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be practiced even without these specific details.

[0029] like Figure 1-11 As shown, an extended-range precipitation forecast system based on the MJO includes a subseasonal prediction unit, a monthly prediction unit, an extended-range forecast unit, an automatic data acquisition unit, a data storage unit, and a login unit. The subseasonal prediction unit, monthly prediction unit, extended-range forecast unit, automatic data acquisition unit, and login unit are all connected to the data storage unit. The subseasonal prediction unit is used to predict precipitation by season based on historical data and generate corresponding charts. The monthly prediction unit is used to predict precipitation by month based on historical data and generate corresponding charts. The extended-range forecast unit is used to forecast MJO precipitation rate, CFSv2 daily forecast, and MJO dynamic similarity forecast. The automatic data acquisition unit is used to automatically download CFSv2.0 model forecast data for the next 50 days at time 0 each day. The data storage unit is used to store and manage data. The login unit is used for user login and permission settings. Chrome version 68.0 or higher is the best browser for optimal performance.

[0030] The data mainly use the pentad index provided by the Climate Prediction Center (CPC) of the National Oceanic and Atmospheric Administration (NOAA), the global atmospheric reanalysis data of the National Centers for Environmental Prediction (NCEP / National Center for Atmospheric Research, NCAR), and the CFSv2.0 grib format forecast data provided by the National Centers for Environmental Prediction of the United States for reanalysis and weather forecasting for the next period.

[0031] The system automatically downloads the CFSv2.0 model forecast data for the next 50 days at 00:00 each day, including data for more than 10 elements, including prate, temperature, wind speed, altitude field, etc. The data grid interval ranges from 0.9375 to 1.0.

[0032] CFSv2.0 Forecast Input: Click the "CFSv2 Forecast" menu to enter the CFSv2 Forecast interface. Set parameters such as region, start date, forecast period, calculation type, and image type. Click Query to output a stamp map of daily precipitation for days 1-45. You can also zoom in on the daily precipitation forecast map.

[0033] In an embodiment of the present invention, the automatic data acquisition unit is used to download the MJO index of the NOAA Climate Prediction Center by pentad. The data period is from 1979 to the day of data download, and data is provided once every pentad. The downloaded data includes prate, temperature, wind speed and height field elements. The downloaded data grid interval is 0.9375-1.0.

[0034] In an embodiment of the present invention, the data storage unit includes a download data storage module, a prediction data storage module and a user data storage module. The download data storage module is used to store the original data downloaded by the automatic data acquisition unit. The prediction data storage module is used to store the predicted generated icon data and perform data time marking. The data is stored as a label according to the predicted time point. The user data storage module is used to store the user's login information and the user's personal information data.

[0035] In an embodiment of the present invention, the extended-range forecast unit includes an MJO precipitation rate forecast module, a CFSv2 daily forecast module, and an MJO dynamic similarity forecast module. The MJO precipitation rate forecast module is used to forecast the MJO precipitation rate, and generates corresponding spatial maps and trend maps based on historical data to display data from similar years. The CFSv2 daily forecast module is used to generate corresponding climate field forecast charts based on specific locations and selected circulation fields. The MJO dynamic similarity forecast module is used to calculate and search for similar years of historical MJO index, calculate the daily precipitation distribution in similar years, and use a synthesis method to obtain a precipitation forecast for the next 50 days.

[0036] In an embodiment of the present invention, the CFSv2 daily forecast module is provided with a region option box, a circulation field option box, a start date, a forecast period, a calculation type, and a graph type option box. When the forecast is completed and the corresponding icon is displayed, the user can choose to download the forecast in txt or bin format.

[0037] In an embodiment of the present invention, the MJO dynamic similarity forecast module is provided with a regional option box, a reporting start date, and a time period line option to generate a spatial map. A trend map can also be generated to display similar annual fee data. In the generated bar chart, the height of the bar each day represents the amount of precipitation. The MJO dynamic similarity forecast module can also perform site rainfall prediction based on the set site.

[0038] In an embodiment of the present invention, in the MJO dynamic similarity forecast module, click the MJO dynamic similarity menu to enter the forecast interface, set the parameters of the region, start date, forecast period, calculation type, and image type, click query to obtain the daily precipitation forecast for each station in the region or river basin. When switching to the daily precipitation forecast and verification of a single station, select a station in the drop-down box to automatically output the 45-day precipitation forecast result for the station. If it is a forecast backcalculation, the actual precipitation is also displayed.

[0039] In an embodiment of the present invention, in the MJO dynamic similarity forecast module, when forecasting precipitation trends for a period, select any period and click on the statistical chart on the interface. The system automatically outputs the precipitation trend forecast results for that period. When outputting the precipitation process forecast verification, switch to the precipitation trend verification. The system automatically outputs the daily average precipitation in the entire area during the selected forecast period.

[0040] In an embodiment of the present invention, in the sub-seasonal prediction unit, when outputting the forecast for years with high MJO correlation, select MJO correlation in the options, click on the distribution map, and the system automatically outputs the precipitation synthesis results for the years with high correlation. When outputting the 45-day daily precipitation forecast verification for a station, switch to the trend verification option, select a station, and the system automatically outputs the 45-day precipitation trend and actual situation for that station.

[0041] The system extends the effective forecast period and is easier to capture in climate models. Its phase is strongly correlated with regional precipitation and can be used as a forecast anchor point. It is capable of multi-scale coupling. The MJO couples tropical weather scales with mid-latitude weather systems, and can simultaneously analyze local heavy rains and regional persistent drought and flood trends, data assimilation and ensemble forecast optimization, extreme event warning potential, and cross-regional climate linkage prediction.

[0042] Matters not covered by the present invention are known technologies.

[0043] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An extended-range precipitation forecast system based on the MJO, characterized by: It includes a sub-seasonal prediction unit, a monthly prediction unit, an extended-term forecast unit, an automatic data acquisition unit, a data storage unit and a login unit. The sub-seasonal prediction unit, the monthly prediction unit, the extended-term forecast unit, the automatic data acquisition unit and the login unit are all connected to the data storage unit. The sub-seasonal prediction unit is used to predict precipitation on a quarterly basis based on historical data and generate corresponding icons. The monthly prediction unit is used to predict precipitation on a monthly basis based on historical data and generate corresponding icons. The extended-term forecast unit is used for MJO precipitation rate forecast, CFSv2 daily forecast and MJO dynamic similarity forecast. The automatic data acquisition unit is used to automatically download the CFSv2.0 model forecast data for the next 50 days at time 0 every day. The data storage unit is used to store and manage data. The login unit is used for customers to log in and set permissions.

2. The MJO-based extended-range precipitation forecast system according to claim 1, characterized in that: The automatic data acquisition unit is used to download the MJO index of the NOAA Climate Prediction Center by pentad. The data period is from 1979 to the day of data download. Data is provided every pentad. The downloaded data includes prate, temperature, wind speed and height field elements. The downloaded data grid interval is 0.9375-1.

0.

3. The MJO-based extended-range precipitation forecast system according to claim 1, characterized in that: The data storage unit includes a download data storage module, a prediction data storage module and a user data storage module. The download data storage module is used to store the original data downloaded by the automatic data acquisition unit. The prediction data storage module is used to store the predicted icon data and perform data time marking. The label is stored according to the predicted time point. The user data storage module is used to store the user's login information and the user's personal information data.

4. The MJO-based extended-range precipitation forecast system according to claim 1, characterized in that: The extended-range forecast unit includes the MJO precipitation rate forecast module, the CFSv2 daily forecast module and the MJO dynamic similarity forecast module. The MJO precipitation rate forecast module is used to forecast the MJO precipitation rate. It generates corresponding spatial maps and trend maps based on historical data, showing data from similar years. The CFSv2 daily forecast module is used to generate corresponding climate field forecast charts based on specific locations and selected circulation fields. The MJO dynamic similarity forecast module is used to calculate and find similar years of historical MJO index, calculate the daily precipitation distribution in similar years, and use a synthetic method to obtain the precipitation forecast for the next 50 days.

5. The MJO-based extended-range precipitation forecast system according to claim 4, characterized in that: The CFSv2 daily forecast module has area option boxes, circulation field option boxes, start date, forecast period, calculation type and graph type option boxes. When the forecast is completed and the corresponding icon is drawn, the user can choose to download it in txt or bin format.

6. The MJO-based extended-range precipitation forecast system according to claim 4, characterized in that: The MJO dynamic similarity forecast module is equipped with regional option boxes, reporting start date and time period line options to generate spatial maps. It can also generate trend charts to display similar annual fee data and generate bar charts. The height of the bars each day represents the amount of precipitation. The MJO dynamic similarity forecast module can also make site rainfall forecasts based on the set sites.

7. The MJO-based extended-range precipitation forecast system according to claim 4, characterized in that: In the MJO dynamic similarity forecast module, click the MJO dynamic similarity menu to enter the forecast interface, set the parameters for region, start date, forecast period, calculation type, and image type, and click query to obtain the daily precipitation forecast for each station in the region or river basin. When switching to the daily precipitation forecast and verification of a single station, select a station in the drop-down box to automatically output the 45-day precipitation forecast result for that station. If it is a forecast backcalculation, the actual precipitation is also displayed.

8. The MJO-based extended-range precipitation forecast system according to claim 4, characterized in that: In the MJO dynamic similarity forecast module, when forecasting precipitation trends for a period of time, select any period and click the statistical chart on the interface. The system will automatically output the precipitation trend forecast results for that period. When outputting the precipitation process forecast test, switch to the precipitation trend test. The system will automatically output the average daily precipitation in the entire area during the selected forecast period.

9. The MJO-based extended-range precipitation forecast system according to claim 1, characterized in that: In the sub-seasonal prediction unit, when outputting forecasts for years with high MJO correlation, select MJO correlation in the options and click on the distribution map. The system will automatically output the precipitation synthesis results for years with high correlation. When outputting the 45-day daily precipitation forecast verification for a station, switch to the trend verification option, select a station, and the system will automatically output the 45-day precipitation trend and actual situation for that station.

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