Based on the air temperature, wind speed, humidity, air pressure, etc., of the regional automatic weather station in Chutouling Town, Jizhou from April 2019 to November 2020, and the air temperature of the microclimate observation station receiving data every 10 min in a bailing mushroom greenhouse, this paper analyzed and evaluated a BP (back propagation) neural network and stepwise regression method to establish a prediction model for the temperature in the Bailing mushroom greenhouse for different seasons. The results showed that: (1) The air temperature, wind speed, humidity and air pressure outside the shed were the main factors for building the temperature prediction model for the inside temperature, and the air temperature was the most important factor affecting the temperature inside the shed. After introducing humidity, wind speed and air pressure, the accuracy of the model was significantly improved. (2) The temperature prediction model based on the BP neural network method, for every 10 min interval in the greenhouse, for the Bailing mushroom in different seasons, was more accurate than the stepwise regression model. The simulation results of the two models had the highest accuracy in summer, followed by autumn. (3) The root means square error of the BP neural network and stepwise regression model for inside the greenhouse, simulating the daily temperature variations for different seasons, was 1.25, 1.10, 1.08, 1.31 °C and 1.29, 1.19, 1.11, 1.37 °C, respectively. The BP neural network method performed better for predicting the daily temperature variations in seasons. (4) The specifying data of high temperature (24 July 2020) and strong cold wave (31 December 2019) were selected to test the two model methods; the results showed that the simulation of the BP neural network model was better than the stepwise regression model.

The Layered Precipitable Water Vapor (LPW) product derived from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the first of the Chinese new generation geostationary satellite Fengyun-4A (FY-4A) has great significance for weather forecasting and climate monitoring of the Tibetan Plateau. To analysis and evaluation the reliability of the FY-4A/AGRI LPW, with respect to the complex terrain on the Southeastern Tibetan Plateau, the atmospheric precipitable water vapor values were calculated based on the radiosonde observations (RAOB TPW) of 11 radiosonde stations in the research area from 2019 to 2020, and a comparative analysis was performed with the FY-4A/AGRI LPW. The results indicated that: (1) FY-4A/AGRI LPW and RAOB TPW demonstrate excellent consistency in all of the vertical height layers, but the atmospheric precipitable water vapor was underestimated by FY-4A/AGRI LPW; (2) The mean values of FY-4A/AGRI LPW in various months were all lower than those of RAOB TPW. The low layer FY-4A/AGRI LPW was the most stable in precision from the dimension of months; and (3) The precision of FY-4A/AGRI LPW, and the deviation between FY-4A/AGRI LPW and RAOB TPW were related with RDLS. The evaluation results of the study demonstrated that FY-4A/AGRI LPW underestimated the total water vapor in the research area, but the Bias and RMSE values were relatively low. FY-4A/AGRI LPW had a relatively high precision, and the data from it had superior quality and stability in terms of time changes and spatial distribution. Therefore, the product can perfectly reflect the spatial and temporal variation of the atmospheric water vapor on the Southeastern Tibetan Plateau.

Quantitative precipitation forecast (QPF) verification stands out as one of the most formidable endeavors in the realm of forecast verification. Traditional verification methods are not suitable for high-resolution forecasting products in some cases. Therefore, the SAL (structure, amplitude and location) method was proposed as a method of object-based spatial verification that studies precipitation verification in a certain range, which is combined with factors including structure, amplitude and location of the targets. However, the setting of the precipitation threshold would affect the result of the verification. This paper presented an improved method for determining the precipitation threshold using the QPF from ECMWF, which is an ensemble forecast model and three-source fusion product that was used in China from 1 July to 31 August 2020, and then the results obtained with this method were compared with the other two traditional methods. Furthermore, the SAL and the traditional verification methods were carried out for geometric, simulated and real cases, respectively. The results showed the following: (1) The proposed method in this paper for determining the threshold was more accurate at identifying the precipitation objects. (2) The verification area size was critical for SAL calculation. If the area selected was too large, the calculated SAL value had little significance. (3) ME (Mean Error) could not identify the displacement between prediction and observation, while HSS (Heidke Skill Score) was sensitive to the displacement of the prediction field. (4) Compared with the traditional verification methods, the SAL method was more straight forward and simple, and it could give a better representation of prediction ability. Therefore, forecasters can better understand the model prediction effect and what needs to be improved.

Drought is the main meteorological disaster that affects the yield and quality of spring maize on the Loess Plateau. This study used data of the spring maize growth period, relative soil humidity, and yield at 18 agricultural meteorological observation stations on the Loess Plateau from 1997 to 2013 to determine the drought category based on the yield reduction rate. Through the drought index according to the conformity rate of category standard and individual case verification, a refined suitability drought index of spring maize on the Loess Plateau was constructed, and the spatial distribution characteristics of drought in different growth stages of spring maize were analyzed. The results showed the following: (1) The number of days in the whole growth period of spring maize in all regions of the Loess Plateau has been extended. The average sowing date of spring maize in the northwest region of the Loess Plateau was 9 April, and that in the east and central regions was 26 April. In terms of spatial distribution, each growth period was gradually delayed from west to east. (2) The correlation between relative soil humidity and yield of spring maize at the jointing stage and heading stage was the best, followed by the milky stage and mature stage, and the relative soil humidity at the sowing stage and emergence stage had little effect on the yield. (3) According to the national drought category standard “Drought Category of Spring Maize in the North”, based on the data of yield reduction rate, the drought index of spring maize on the Loess Plateau was refined by region and growth stage. The drought category index values of spring maize in different growth stages and regions changed according to the revised drought category standard, with 71.4% of the sites in the sowing seedling stage and 85.7% of the sites in the seedling jointing stage, and the revised drought category was more severe than the national drought category standard, while at 57.1% of the sites in the jointing and tasseling stages and 71.4% in the tasseling and milking stages, the revised drought category was less severe than the national drought category standard. (4) Based on the revised refined drought index for spring maize on the Loess Plateau, the spatial distribution of drought occurrence frequency across different growth stages of spring maize on the Loess Plateau was analyzed. The frequency of drought occurrence during the seeding and emergence stages was 25–75%. With the change in growth stages, the high-value area of drought occurrence frequency gradually moved northward, and the overall frequency of drought occurrence decreased. For the milky mature stage, the frequency of drought occurrence in a few regions was around 42%, and the drought frequency in most regions was between 8% and 33%.

Evapotranspiration is a key part of the water cycle between the atmosphere and the land surface, and it is an important parameter for studying the land–atmosphere system. Change and evolution have important implications. Therefore, the understanding and research of actual evapotranspiration (AET) can profoundly affect water use, ecological environment, temperature, and precipitation. In this paper, the single-layer monthly average reanalysis meteorological data of the Qinghai–Tibet Plateau from 1981 to 2020 was used to study and calculate the actual evapotranspiration in the Qinghai–Tibet Plateau, and the temporal and spatial variation characteristics, variation laws, and changes were analyzed by methods such as cumulative anomalies and the Mann–Kendall trend test. The results showed the following: (1) The evapotranspiration gradually decreased from southeast to northwest. The evapotranspiration in the southeastern region is strong, and the maximum value appears in the Hengduan Mountains. (2) The evapotranspiration was the largest in summer and gradually decreased from southeast to northwest; the evapotranspiration in spring and autumn was relatively uniform, with little overall difference, and the evapotranspiration was the lowest in winter. (3) There were mainly three spatial distribution modes of evapotranspiration in the Qinghai–Tibet Plateau, which were characterized by a significant and consistent change centered on the Tibet region, an east–west reverse type, and an east–west “negative-positive-negative” of the distributed three-pole space. The corresponding time coefficients characterized the interdecadal and interannual variation, and the decadal variation characteristics are more significant than the annual variation characteristics. (4) The actual evapotranspiration had step change; the step change years were 1989, 2002, 2011, and 2015, and there was an interval of about 5 years.

Previous studies have pointed out that persistent abnormal precipitation is closely related to atmospheric low-frequency oscillation, and Southwest China can be affected by low-frequency oscillation in different latitudes and altitudes. Therefore, it is necessary to study the relationship between persistent abnormal precipitation in Southwest China and low-frequency oscillation. In this study, the characteristics of atmospheric low-frequency oscillation in different latitudes and altitudes affecting persistent extreme precipitation in Southwest China were discussed by synthetic analysis and Butterworth filtering with precipitation data from meteorological stations and NCEP/NCAR reanalysis data, and the configuration relationship of low-frequency systems in three-dimensional space was shown. The results showed the following: 1) in the low-frequency circulation field, the Ural Mountains, east of Lake Baikal, the Sea of Okhotsk (Japan), the Western Pacific subtropical high control area, the South China Sea, the Indian Peninsula, and the Bay of Bengal were the key areas affecting precipitation; 2) oscillation could better reflect the original circulation characteristics at all latitudes of each layer, and on a period of oscillation of 15–30 days, it will affect precipitation at all altitudes and latitudes; and 3) at the lower level, different low-frequency cyclones and anticyclones moved to the mid-latitude and converged in different forms in the precipitation area. In the middle layer, the low-frequency high-pressure and low-pressure centers in the middle and high latitudes moved southward, finally forming a circulation situation of the high west and low east. At the upper level, the rainy area was controlled by low-frequency anticyclones and divergence centers from different regions, forming a high-altitude divergence field. The results can provide a theoretical basis for forecasting the extended period of persistent heavy rain in Southwest China.