In order to improve the performance of the regional mesoscale numerical prediction system on temperature forecasts, the CLDAS (CMA Land Data Assimilation System) land data in Zhejiang Province is evaluated for its precision using observed soil temperature data as well as soil moisture data, and then it is applied in Zhejiang numerical prediction system. The results are shown as follows: The CLDAS soil temperature and soil moisture products have smaller root mean squared errors and higher correlation coefficients compared to the observations than those products of the GFS (Global Forecast System) analysis field, and they have good applicability in Zhejiang Province. The case study indicates that 2 m temperature predicted by the regional numerical model is sensitive to the change of land data. It exerts a sustainable influence on the model forecasts at a later stage through blending the CLDAS real-time surface temperature, soil temperature and soil moisture analysis products on the initial field. More specifically, the temperature changes are directly affected by the surface sensible heat flux and latent heat flux. Besides, the root mean squared error of the model prediction using the CLDAS data is lowered by 13.6% in comparison to that of the model prediction using the GFS analysis field as the initial land surface field. The phased application results in July 2021 show the model initial field blending the CLDAS data can effectively improve the forecast accuracy of the 2 m temperature in Zhejiang Province. Moreover, the results present a more favorable improvement at night than in the daytime, and more effective forecasts can be presented under dry-hot weather condition than tropical cyclones and Mei-Yu fronts periods.The evaluation of high temperature forecasts further indicates that the application of CLDAS land data has a good improvement effect on the predictions of high temperature events in Zhejiang Province, especially in high-temperature areas such as the Jinqu Basin.
Since the launch of the Global Precipitation Measurement (GPM) satellite in February 2014, it has provided the foundation for three-dimensional observations of precipitation in high latitudes. The four precipitation products retrieved by the double-frequency precipitation radar (DPR) carried by the GPM satellite can provide a reference for precipitation research in middle and high latitude regions. In this paper, four kinds of precipitation products (Ka_HS, Ka_MS, Ku_NS and DPR_MS) provided by GPM DPR are used to analyze the precipitation structural characteristics of two different types of precipitation weather processes in northeast China and explore the differences in macro and micro characteristics of precipitation, then the performance differences among four precipitation products are further compared. The results show that the spatial distribution of spring stratiform precipitation in northeast China is extensive, the precipitation intensity of most samples is less than 4.00 mm·h-1, and the cloud system develops relatively gently. While the precipitation intensity distribution of mixed precipitation in summer is highly uneven, the development in the vertical direction is relatively strong, and the echo top height can reach 12 km. The Ka_MS precipitation product has apparent flaws in precipitation intensity, echo top height and precipitation type classification. The Ka_HS precipitation product can detect more weak precipitation samples but it will also experience severe attenuation when the rain intensity is high. The Ku_NS precipitation product has better detection capabilities and can provide reliable echo top heights and precipitation type classification. The DPR_MS precipitation product has an independent performance. For weak precipitation intensity less than 0.50 mm·h-1, it has a good correlation with the Ka_HS product, and for precipitation intensity greater than 1.00 mm·h-1, it has a good consistency with the Ku_NS product. In addition, the DPR_MS product has the most reasonable results about the vertical structure of precipitation and the raindrop spectrum.
Based on the precipitation data from Shaanxi Province from 2016 to 2021 and real-time products of two-source and three-source precipitation from the CMA Multi-source Precipitation Analysis System (CMPAS-V2.1) during 2018-2021, the spatial and temporal characteristics of short-term heavy rainfall in Shaanxi Province over the past 6 years were analyzed. Furthermore, statistical tests were applied to evaluate the accuracy of the multi-source precipitation products, with the aim of providing reference for correction of multi-source precipitation products in short-term heavy rainfall processes. The results are as follows:(1) In Shaanxi Province, the short-term heavy rainfall frequency peak was at 19:00 pm, with heavy precipitation primarily occurring from 16:00 to 02:00 in a day and predominantly in the months of June to August. The diurnal extreme value of heavy rainfall shows relatively higher from 17:00 to 01:00 and from 04:00 to 07:00 in the morning. Short-term heavy rainfall is more frequent in southern Shaanxi compared to Guanzhong and northern Shaanxi. The regions with relatively larger extreme values include northern Shaanxi, eastern Guanzhong, and central-eastern of southern Shaanxi. (2) Both two types of precipitation products tend to underestimate precipitation compared to observed values. The mean absolute errors of the three-source product exhibit smaller in southern of northern Shaanxi, most part of Guanzhong and southern of southern Shaanxi, while the two-source product performs relatively better in other region.The mean absolute error increases with precipitation increase. For heavy precipitation ranging from 20 to 50 mm, three-source product is recommended, while two-source product is more suitable for heavy precipitation above 50 mm. (3) The diurnal variations in mean absolute errors for both two types of multi-source products are relatively larger from 13:00 to 19:00, 23:00 to 01:00 and from 04:00 to 06:00, and relatively smaller from 08:00 to 12:00, 20:00 to 22:00 and from 02:00 to 03:00. The three-source product outperforms two-source product from May to August, while two-source product performs better from September to October. (4) The accuracy rate of multi-source precipitation products increases as the threshold of short-term heavy rainfall decrease. Both mean absolute error and accuracy rate indicate that the three-source product outperforms two-source product. Multi-source precipitation products exhibit higher accuracy from September to October compared to May to August.
Under the background of vigorously promoting the construction of ecological civilization, it is of great reference value to study the applicability of vegetation indices in vegetation monitoring in Hainan Island. Based on the NDVI (Normalized Differnce Vegetation Index), EVI (Enhanced Vegetation Index), DVI (Difference Vegetation Index) data extracted from MODIS (Moderate-Resolution Imaging Spectroradiometer), monthly mean temperature and precipitation data of 18 national meteorological observation stations from 2001 to 2020 and land cover data in 2015 and 2017 of Hainan Island, the applicability of three vegetation indices NDVI, EVI and DVI in vegetation monitoring of Hainan Island was studied by using the univariate linear fitting, root mean square error analysis and correlation analysis method. The results are as follows: (1) Among the three vegetation indices, EVI has the best fitting effect on the vegetation coverage area extracted from the land cover data of Hainan Island, the root mean square error accounts for 9.57% of the actual mean. (2) EVI can best reflect the seasonal variation characteristics of vegetation in Hainan Island, it began to increase slowly in February, slow decline after reaching its peak in August, and fell to the lowest in February next year; EVI has the widest range, which can best reflect the vegetation coverage with different thicknesses, and can better overcome the NDVI saturation problem in high vegetation area; EVI has the greatest correlation with climate factors and can best reflect the characteristics of vegetation's response to climate. (3) The fitting effect of three vegetation indices on the vegetation coverage area of Hainan Island was comprehensively evaluated, and the ability to characterize the seasonal variation and density of vegetation was evaluated. The EVI was determined to be the most suitable vegetation index for characterizing the vegetation characteristics of Hainan Island.
The Aircraft Meteorological Data Relay (AMDAR) data has the characteristics of high sampling frequency and dense detection levels, which can effectively supplement the lack of spatial and temporal resolution of high-altitude data. In this paper, the quality analysis and control of AMDAR observation data at different heights were carried out by using the China regional AMDAR data provided by the National Meteorological Information Center and the Global Forecast System (GFS) data provided by National Centers for Environmental Prediction (NCEP) from March 1 to May 31, 2020 (spring). The influence of the assimilation of AMDAR data after quality control on the analysis field and the forecast field was tested by a two-week cycle assimilation comparison test. The results show that the root mean square error (RMSE) between AMDAR wind speed and GFS data shows an obvious increasing trend with the increase of height, and the bias of wind speed and temperature also shows an increasing trend with the increase of height. After quality control of AMDAR data at different heights, the RMSE and bias between AMDAR data and GFS data have been improved, and the observation minus background (OMB) between AMDAR data and GFS data was more consistent with Gaussian distribution. The assimilation of AMDAR data after quality control has a certain improvement on the analysis field of wind, temperature and geopotential height, and the improvement could affect the 12-hour or even 24-hour forecast field. The assimilation of AMDAR data after quality control could improve the precipitation forecasting skills, especially for medium precipitation forecast.
Improving the accuracy of precipitation level forecast is helpful to optimize disaster warning and decision support. Based on the precipitation observation data in the time interval of 12 hours from January 2018 to January 2021 in Shandong Province and the ensemble prediction ensemble mean results of the European Centre for Medium-Range Weather Forecasting, the precipitation level forecast for 12 hours interval within 72 hours are statistically revised. Then, the effects of the original forecast of ECMWF ensemble mean precipitation forecast interpolation (EC_EPEM), the Model Output Statistics (MOS) prediction based on the EC_EPEM (EC_EPEM_MOS) and the Optimal Threat Score (OTS) prediction (EC_EPEM_OTS) are compared, and the improving effects of two statistical correction methods on precipitation level with the time interval of 12 hours prediction of the ECMWF ensemble forecast are discussed. The results indicate that the EC_EPEM_MOS has the best performance on the relatively smaller precipitation grades, while its correction effect is relatively poor for higher grades, even slightly lower than the EC_EPEM. The correction effect of the EC_EPEM_OTS is only lower than the EC_EPEM_MOS for 0.1 and 10.0 mm precipitation grades, and for the other grades it is optimal, especially for the larger grades, its correction effect is more obvious. The EC_EPEM_OTS has the best correction effect from 12 to 72 hours for both 50 mm and 100 mm precipitation grades, because the EC_EPEM_OTS increases the correction coefficient for a slightly larger grade, resulting in a low false report rate for large grades. At the same time, using a smaller correction coefficient for large precipitation also reduces the false report rate. The EC_EPEM_MOS is best in most parts of Shandong Province except for the mountains area in the middle parts for short prediction period and smaller precipitation, while the EC_EPEM_OTS is the best in the mountains area. For above medium grade, especially large precipitation, the EC_EPEM_OTS is the best in most areas of Shandong Province. The EC_EPEM_MOS correction prediction effectively reduces the problem of empty report of the EC_EPEM. The correction effect of the EC_EPEM_OTS is the best, and the rainfall area is closer to the observations in the processes of large-scale heavy rainfalls, and the overall distribution of precipitation is better grasped.
Based on the quasi-symmetric mixed sliding training period method, the grid analysis products of daily maximum temperature and daily minimum temperature from Land Data Assimilation System of China Meteorological Administration (CLDAS) in recent two years are revised, in order to improve the applicability of the products in Chongqing. The results show that before revision, the average error of daily maximum temperature products in 2021 was 0.63 ℃, and the average absolute error was 1.14 ℃. After revision, the average error decreased to -0.03 ℃, the average absolute error decreased to 0.64 ℃, the accuracy of error less than or equal to 1 ℃ was improved from about 64% to 90%, which obviously improved the applicability of products in western and northeastern Chongqing. Before revision, the average error of daily minimum temperature products in 2021 was -0.22 ℃, and the average absolute error was 0.75 ℃. After revision, the average error was reduced to -0.03 ℃, the average absolute error was reduced to 0.55 ℃, and the accuracy of error less than or equal to 1 ℃ was improved from about 91% to 93%, which improved the applicability of products in central Chongqing.
