The terrain in northwest inland river of China is complex, where there are few meteorological stations. In order to improve the accuracy of precipitation spatialization, support hydrological modeling and water resource management in arid area, based on the datasets of 19 meteorological stations and 26 hydrological stations in the Heihe River Basin, the partial thin plate smoothing splines function models are constructed using ANUSPLIN. The influence of model parameters, station numbers and the resolution of Digital Elevation Model (DEM) on precipitation spatial interpolation accuracy is analyzed. The results are as follows: 1) The V2C1S3_RT model, with longitude and latitude as independent spline variables, elevation as the independent covariate, the spline order of 3, and square root as the variable transform method, achieves the highest interpolation accuracy, with square roots of generalized cross validation (RTGCV) and square roots of mean square error (RTMSE) values of 6.10 and 4.82 mm, respectively. 2) The number of stations significantly affects interpolation accuracy. When the number of stations increases to 40, the RTGCV and RTMSE are the minimum, and further increasing the number of stations has limited improvement on accuracy. 3) Different resolution DEM has little effect on precipitation interpolation results. 4) The regional mean precipitation in the Heihe River Basin in 2019 was 211.39 mm, decreasing from southwest to northeast. Due to sparse station distribution, the standard error of precipitation in the southwestern upstream and downstream boundary areas is significantly higher than that in other regions.
The accuracy evaluation of satellite-derived cloud cover is foundation of operational applications, and it is of great significance for effectively leveraging the high spatiotemporal resolution advantages of satellite remote sensing and compensating for the scarcity of ground-based observations. This study focuses on the Ningxia region, a typical arid and semiarid region, and utilizes ground observations of total cloud cover amount at five time points (08:00, 11:00, 14:00, 17:00, and 20:00, Beijing time, the same below) in 2019 to validate and evaluate the Fengyun-4A ( referred to as “FY-4A”) satellite cloud cover fraction product, then uses the normalized mixed correction method to correct the monthly cloud cover fraction products and analyze the daytime spatiotemporal distribution characteristics of cloud cover fraction in Ningxia. The results indicate that: 1) The monthly and daily variation trends of the FY-4A cloud cover fraction product in Ningxia region are highly consistent with those of ground manual observations of total cloud amount. The correlation coefficients between FY-4A satellite-derived cloud cover fraction and ground observed total cloud amount range from 0.7 to 0.9 across the whole region and at individual meteorological stations, but the derived cloud cover fractions are generally lower than the observed total cloud amounts. 2) In terms of the consistency rate between the total cloud amount and cloud cover fraction at five different times of a day, the consistency rates at 11:00, 14:00, and 17:00 are higher than those at 08:00 and 20:00. For cloud cover categories test, the consistency rates are in the order of clear sky > overcast > partly cloudy > mostly cloudy. 3) The normalized mixed correction method effectively reduces the bias between the FY-4A cloud cover fraction product and the manual total cloud amount observations. After correction, the monthly average values of the cloud cover fraction product are in good agreement with the cloud amounts observed at meteorological stations. 4) The annual average daytime cloud coverage in Ningxia ranges from 30% to 70%. Higher cloud cover fractions are found along the Helan Mountains, in the Yellow River irrigation area (from Shapotou District, Zhongwei, to Yinchuan), and in Haiyuan County and Guyuan City. The lowest cloud cover occurs in the eastern parts of Huinong District, Dawukou District, and Pingluo County in Shizuishan City. Cloud cover fraction is generally higher in May and June, and lowest in December.
To compensate for the limitations of the Weather Phenomena Video Intelligent Observation Instrument in purely visual cloud classification, an AI (Artificial Intelligence) cloud classification sample library was constructed by integrating millimeter-wave cloud radar and all-sky imager measurements from the Zhangye National Climate Observatory experimental field during the period from May 1, 2023 to April 30, 2024, together with manual cloud observations and ground automatic station meteorological data. Multiple machine learning algorithms were applied for training and performance evaluation, and the Support Vector Machine model was identified as the most effective in terms of overall accuracy and stability. This model enables the automatic recognition of nine cloud types, cirrocumulus, cirrus, altocumulus, altostratus, nimbostratus, stratus, stratocumulus, cumulonimbus, and cumulus, as well as precipitation weather. Verification based on four typical daily cloud classification cases demonstrated that the model can accurately identify multilayer cloud structures, with results highly consistent with manual observations. This study achieves significant improvements in both data integration and algorithm adaptability, increasing the number of identifiable cloud types by 33% and enhancing classification accuracy by 15%.
In order to promote the development and utilization of ecological climate resources in Kangxian County, meteorological data from 1991 to 2020, as well as data about forest resources, water quality and air environment quality were used to evaluate the ecological climate resources in Kangxian County from two aspects: ecological environment characteristic resources and climate resources. The results are as follows: The average annual negative (oxygen) ion number concentration was 3 338 per cubic centimeter in Kangxian County, which met the air freshness standard for negative (oxygen) ion concentration and was extremely beneficial to human health. The air quality in Kangxian County was excellent, and the number of days with good air accounted for 99.2% of the whole year. Kangxian County had abundant rainfall, suitable temperature and humidity, and very few hot days in summer. In addition, the inter-annual variations of temperature, precipitation, and relative humidity showed a gradual upward trend. The increasing rates of average temperature, average maximum temperature and average minimum temperature were 0.16, 0.32 and 0.27 ℃ per decade, respectively. The increase in precipitation was approximately 78.2 mm per decade, and the increase in relative humidity was approximately 0.9% per decade. Compared with neighboring counties (districts), Kangxian County has the most abundant precipitation, the lowest dryness value, the lowest average temperature, average maximum temperature, and average minimum temperature, the highest average relative humidity, and fewer incidents of severe weather. The climate comfort period lasted up to 9 months. Compared with some tourist cities in China, Kangxian County has obvious advantages in terms of climate and wellness conditions, such as summer retreat, oxygen rich health care, moist nourishment, vacation and rest, and sightseeing and leisure.
