A New Merged Product Reveals Precipitation Features over Drylands in China

Due to the considerable uncertainties inherent in the datasets describing the spatiotemporal distributions of precipitation in the drylands of China,this study presents a new merged monthly precipitation product with a spatial resolution of approximately 0.2°×0.2°during 1980–2019.The newly developed precipitation product was validated at different temporal scales(e.g.,monthly,seasonally,and annually).The results show that the new product consistently aligns with the spatiotemporal distributions reported by the Chinese Meteorological Administration Land Data Assimilation System(CLDAS)product and Multi-Source Weighted Ensemble Precipitation(MSWEP).The merged product exhibits exceptional quality in describing the drylands of China,with a bias of–2.19 mm month^(–1)relative to MSWEP.In addition,the annual trend of the merged product(0.09 mm month^(–1)yr^(−1))also closely aligns with that of the MSWEP(0.11 mm month^(–1)yr^(−1))during 1980–2019.The increasing trend indicates that the water cycle and wetting process intensified in the drylands of China during this period.In particular,there was an increase in wetting during the period from 2001–2019.Generally,the merged product exhibits potential value for improving our understanding of the climate and water cycle in the drylands of China.

Intercomparison of CRA-Interim Precipitation Products with ERA5 and JRA-55

Based on the hourly observational data during 2007-2016 from surface meteorological stations in China,this paper compares the influence of 3-hourly precipitation data,mainly from the Chinese Reanalysis-Interim(CRA-Interim),ECMWF Reanalysis 5(ERA5)and Japanese Reanalysis-55(JRA-55),on the simulation of the spatial and temporal distribution of regional precipitation in China and the bias distribution of the simulation.The results show that:(1)The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual average precipitation in China.The simulation of topographic forced precipitation in complex terrain by using CRA-interim is more detailed,while CRA-interim has larger negative bias in central and East China,and larger positive bias in southwest China.(2)In terms of seasonal precipitation,the three sets of reanalysis datasets overestimate the precipitation in the heavy rainfall zone in spring and summer,especially in southwest China.According to CRA-interim,location of the rain belt in the First Rainy Season in South China is west by south,and the summer precipitation has positive bias in southwest and South China.(3)All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of drought and flood,but overall the CRA-Interim generally shows negative bias,while the ERA5 and JRA-55 exhibit positive bias.(4)For the diurnal variation of precipitation in summer,all the reanalysis datasets perform better in simulating the daytime precipitation than in the night,and the bias of CRA-interim is less in the Southeast and Northeast than elsewhere.(5)The ERA5 generally performs the best on the evaluation of quantitative precipitation forecast,the JRA-55 is the next,followed by the CRA-Interim.The CRA-Interim has higher missing rate and lower threat score for heavy rains;however,at the level of downpour,the CRA-Interim performs slightly better.

An observational study of precipitation types in the Alaskan Arctic

The effects of various precipitation types,such as snow,rain,sleet,hail and freezing rain,on regional hydrology,ecology,snow and ice surfaces differ significantly.Due to limited observations,however,few studies into precipitation types have been conducted in the Arctic.Based on the high-resolution precipitation records from an OTT Parsivel^(2) disdrometer in Utqiaġvik,Alaska,this study analysed variations in precipitation types in the Alaskan Arctic from 15 May to 16 October,2019.Results show that rain and snow were the dominant precipitation types during the measurement period,accounting for 92%of the total precipitation.In addition,freezing rain,sleet,and hail were also observed(2,4 and 11 times,respectively),accounting for the rest part of the total precipitation.The records from a neighbouring U.S.Climate Reference Network(USCRN)station equipped with T-200B rain gauges support the results of disdrometer.Further analysis revealed that Global Precipitation Measurement(GPM)satellite data could well characterise the observed precipitation changes in Utqiaġvik.Combined with satellite data and station observations,the spatiotemporal variations in precipitation were verified in various reanalysis datasets,and the results indicated that ECMWF Reanalysis v5(ERA5)could better describe the observed precipitation time series in Utqiaġvik and the spatial distribution of data in the Alaskan Arctic.Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)overestimated the amount and frequency of precipitation.Japanese 55-year Reanalysis(JRA-55)could better simulate heavy precipitation events and the spatial distribution of the precipitation phase,but it overestimated summer snowfall.

Evaluation of six gauge-based gridded climate products for analyzing long-term historical precipitation patterns across the Lancang-Mekong River Basin

Freshwater plays a vital role in global sustainability by improving human lives and protecting nature.In the Lancang-Mekong River Basin(LMRB),sustainable development is principally dependent upon precipitation that predominantly controls freshwater resources availability required for both life and livelihood of~70 million people.Hence,this study comprehensively analyzed long-term historical precipitation patterns(in terms of trends,variability,and links to climate teleconnections)throughout the LMRB as well as its upper(Lancang River Basin,LRB)and lower(Mekong River Basin,MRB)parts employing six gauge-based gridded climate products:Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources(APHRODITE),Climate Prediction Center(CPC),Climate Research Unit(CRU),Global Precipitation Climatology Center(GPCC),Precipitation Reconstruction over Land(PRECL),and University of Delaware(UDEL).Accordingly,annual and seasonal(dry and wet)precipitation time series were calculated for three study periods:century-long outlook(1901-2010),mid-past(1951-2010),and recent decades(1981-2010).However,the role of climate teleconnections in precipitation variability over the LMRB was only identified during their available temporal coverages:mid-past and recent decades.The results generally showed that:(i)both annual and seasonal precipitation increased across all three basins in 1981-2010;(ii)wet and dry seasons got drier and wetter,respectively,in all basins in 1951-2010;(iii)all such changes were fundamentally attributed to increases in precipitation variability on both annual and seasonal scales over time;(iv)these variations were most strongly associated with the Pacific Decadal Oscillation(PDO),Atlantic Multi-decadal Oscillation(AMO)and East Pacific/North Pacific(EP/NP)pattern in the LMRB and the MRB during 1951-2010,but with the North Sea-Caspian Pattern(NCP)and the Southern Annular Mode(SAM)in the LRB;(v)such relationships got stronger in 1981-2010,while the Southern Oscillation Index(SOI)became the most influential teleconnection for dry season precipitation variability across all basins;and(vi)GPCC(APHRODITE)provided the most reliable gauge-based gridded precipitation time series over the LMRB for the years before(after)1951.These findings lay a foundation for further studies focusing on water resources and sustainable development in the LMRB.

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known"double penalty"problem caused by the slight spatial or temporal mismatches between forecasts and observations.The fuzzy(neighborhood)method has been proposed for deterministic simulations and shown some ability to solve this problem.The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts.We developed an ensemble precipitation verification skill score,i.e.,the Spatial Continuous Ranked Probability Score(SCRPS),and used it to extend spatial verification from deterministic into ensemble forecasts.The SCRPS is a spatial technique based on the Continuous Ranked Probability Score(CRPS)and the fuzzy method.A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency,which were then used in the reference score to calculate the skill score of the SCRPS.The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained.The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.

基于降水量距平百分率的1961—2020年鄂尔多斯气象干旱趋势

鄂尔多斯由于干旱事件频发,严重影响到当地的经济发展。研究鄂尔多斯的干旱时空演变特征对抗旱减灾工作的开展十分必要。本文采用1961—2020年鄂尔多斯85个格点的逐月降水数据,利用游程理论分析了标准化降水指数(Standardized Precipitation Index,SPI)和降水量距平百分率指数(Precipitation Anomaly Percentage,Pa)在该地区的适用性,并进一步采用反距离插值等方法分析干旱时空演变特征,结果表明:(1)Pa指数对鄂尔多斯干旱的描述具有很强的敏感性和准确性。(2)干旱年际变化频率高,夏旱发生频率最高,为16.7%,冬旱发生频率最低,为13.3%,其中,干旱发生率随干旱等级升高而逐渐降低。(3)鄂尔多斯西部比东部更易发生严重干旱,不受旱灾影响的概率逐渐由42%增大为75%。(4)春夏两季的干旱情况对整年的干旱情况影响最大,秋冬两季的干旱形势有所好转,并且冬季好转更明显。

Stable hydrogen isoscape in precipitation generated using data fusion for East China

The stable hydrogen isotope in precipitation is an effective environmental tracer for climatic and hydrologic studies.However,accurate and high-precision precipitation hydrogen isoscapes are currently unavailable in China.In this study,a data fusion method based on Convolutional Neural Networks(CNN)is used to fuse the hydrogen isotopic composition(δ^(2)H_(p))of observations and isotope-equipped general circulation model(iGCM)simulations.A precipitation hydrogen isoscape with a temporal resolution of monthly and a spatial resolution of 50-60 km is established for East China for the 1969-2017 period.Prior to building the isoscape,the performance of three data fusion methods(DFMs)and two bias correction methods(BCMs)is compared.The results indicate that the CNN fusion method performs the best with a correlation coefficient larger than 0.90 and root mean square error smaller than 10.5‰ when using observation as a benchmark.The fusion methods based on back propagation and long short-term memory neural network perform similarly,while slightly outperforming the bias correction methods.Thus,the CNN method is used to generate the hydrogen isoscape,and the temporal and spatial distribution characteristics of the hydrogen isotope in precipitation are analyzed based on this dataset.The generated isoscape shows similar spatial and temporal distribution characteristics to observations.In general,the distribution pattern of δ^(2)H_(p) is consistent with the temperature effect in northern China,and consistent with the precipitation amount effect in southern China.The trend of the δ^(2)H_(p) time series is consistent with that of observed precipitation and temperature.Overall,the generated isoscape effectively reproduces the observations,and has the characteristics of time continuity and relative spatial regularity,which can provide valuable data support for tracking atmospheric and hydrological processes.

Evaluation of CLDAS and GPM Precipitation Products over the Tibetan Plateau in Summer 2005–2021 Based on Hourly Rain Gauge Observations

Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).The Global Precipitation Measurement(GPM) data are widely recognized as the most reliable satellite precipitation product for the TP.The China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS) precipitation fusion dataset(CLDAS-Prcp),hereafter referred to as CLDAS,is a high-resolution,self-developed precipitation product in China with regional characteristics.Focusing on the TP,this study provides a long-term evaluation of CLDAS and GPM from various aspects,including characteristics on different timescales,diurnal variation,and elevation impacts,based on hourly rain gauge data in summer from 2005 to 2021.The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP.They both perform well for precipitation amount and frequency on multiple timescales.CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP.However,GPM tends to overestimate both precipitation amount and frequency.The difference between them mainly lies in the trace precipitation.CLDAS and GPM effectively capture rainfall events,but their performance decreases significantly as intensity increases.They both show better accuracy in diurnal variation of precipitation amount than frequency,and their performance tends to be superior during nighttime compared to the daytime.Nevertheless,there are some differences of the two against rain gauge observations in diurnal variation,especially in the phase of the diurnal variation.The performance of CLDAS and GPM varies at different elevations.They both have the best performance over 3000–3500 m.The elevation dependence of CLDAS is relatively minor,while GPM shows a stronger elevation dependence in terms of precipitation amount.GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations.CLDAS and GPM exhibit unique strengths and weaknesses;hence,the choice should be made according to the specific situation of application.

Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological datasets are not more accurate than synoptic stations,but their various advantages,such as spatial coverage,time coverage,accessibility,and free use,have made these techniques superior,and sometimes we can use them instead of synoptic stations.In this study,we used four meteorological datasets,including Climatic Research Unit gridded Time Series(CRU TS),Global Precipitation Climatology Centre(GPCC),Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications(AgMERRA),Agricultural Climate Forecast System Reanalysis(AgCFSR),to estimate climate variables,i.e.,precipitation,maximum temperature,and minimum temperature,and crop variables,i.e.,reference evapotranspiration,irrigation requirement,biomass,and yield of maize,in Qazvin Province of Iran during 1980-2009.At first,data were gathered from the four meteorological datasets and synoptic station in this province,and climate variables were calculated.Then,after using the AquaCrop model to calculate the crop variables,we compared the results of the synoptic station and meteorological datasets.All the four meteorological datasets showed strong performance for estimating climate variables.AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature.However,their normalized root mean square error was inferior to CRU for minimum temperature.Furthermore,they were all very efficient for estimating the biomass and yield of maize in this province.For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR.But for the estimation of biomass and yield,all the four meteorological datasets were reliable.To sum up,GPCC and AgCFSR were the two best datasets in this study.This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.

Evaluation of Daily Gridded Meteorological Datasets over the Niger Delta Region of Nigeria and Implication to Water Resources Management

Hydro-climatological study is difficult in most of the developing countries due to the paucity of monitoring stations. Gridded climatological data provides an opportunity to extrapolate climate to areas without monitoring stations based on their ability to replicate the Spatio-temporal distribution and variability of observed datasets. Simple correlation and error analyses are not enough to predict the variability and distribution of precipitation and temperature. In this study, the coefficient of correlation (R2), Root mean square error (RMSE), mean bias error (MBE) and mean wet and dry spell lengths were used to evaluate the performance of three widely used daily gridded precipitation, maximum and minimum temperature datasets from the Climatic Research Unit (CRU), Princeton University Global Meteorological Forcing (PGF) and Climate Forecast System Reanalysis (CFSR) datasets available over the Niger Delta part of Nigeria. The Standardised Precipitation Index was used to assess the confidence of using gridded precipitation products on water resource management. Results of correlation, error, and spell length analysis revealed that the CRU and PGF datasets performed much better than the CFSR datasets. SPI values also indicate a good association between station and CRU precipitation products. The CFSR datasets in comparison with the other data products in many years overestimated and underestimated the SPI. This indicates weak accuracy in predictability, hence not reliable for water resource management in the study area. However, CRU data products were found to perform much better in most of the statistical assessments conducted. This makes the methods used in this study to be useful for the assessment of various gridded datasets in various hydrological and climatic applications.

西藏主要流域年径流的变化趋势及其原因

青藏高原是受气候变化影响最为明显的地区之一,气候变化驱动下的径流变化趋势及其原因备受关注。该研究采用Mann-Kendall检验和Pettitt突变点检验,分析了西藏自治区11个水文站1980—2016年径流深的变化规律;并采用基于Choudhury-Yang方程的气候弹性法,量化了年降水、年潜在蒸发以及下垫面变化对年径流深的影响。结果表明,研究区年径流深总体呈不显著上升趋势,多个流域在1997年左右发生突变;雅鲁藏布江下游地区年径流深对气候变化和下垫面变化的敏感性明显低于其他地区;年降水量的变化是年径流深变化的主要影响因素,导致所有站点的年径流深增加,但区域差异较为明显,其中对拉萨河流域的影响最大。

基于多源数据的青藏高原雨季降水特征变化分析

高原地区地形复杂,气象测站的长期实地观测数据有限,且主要集中在东部区域。虽然近年来各种降水分析数据集得到了发展,但这些数据集的代表性及数据之间的一致性等问题制约了对高原地区的降水特征变化的认识。基于国家气象信息中心提供的青藏高原地区89个地面气象站降水数据及西部9个加密自动站数据,综合分析了多套逐日格点分析数据集(APHRO、CN05.1、CMFD、TRMM及GPCP)反映的雨季降水量、降水日数、极端降水量及极端降水日数与实际观测之间的一致性与差异,并深入研究了其区域变化特征。结果表明:(1)CN05.1及CMFD数据与高原实地观测的数据在区域平均降水量变化,降水量和极端降水量的空间分布格局及其变化趋势分布等方面均具有较好的一致性,但在降水日数与极端降水日数方面均存在一定差异;APHRO降水数据集揭示的高原雨季降水量和降水日数变化趋势偏小,极端降水量与日数的变化趋势与实地观测的趋势相反;GPCP与TRMM降水分析数据序列较短,反映的高原地区极端降水变化趋势与观测较一致,但高原总体降水量变化与观测结果反向,降水日数变化则被明显高估。(2)以实地降水观测为基准,考虑其他多套分析数据集的一致性特点,综合评估得到:对于高原整体,1961—2019年雨季降水量、极端降水量、极端降水日数总体上呈现增加的趋势;但对降水日数,由于实地观测得到的变化与其他分析数据结果之间差异较大,对其变化的认识仍存在不确定性。从不同气候分区上看,高原干旱区与半干旱区的降水量、降水日数、极端降水量、极端降水日数总体增加;对于半湿润区,极端降水日数微弱增加,降水量与极端降水量明显增加,但在其东部和中部地区微弱减少,而降水日数由于在其东部和南部显著减少引起半湿润区总体上呈现出减少趋势。

小时和日值降水数据对河南省暴雨日数和暴雨日变化特征的影响

利用1961—2018年河南省111个气象站逐日和逐小时降水数据,分析了暴雨日数的时空变化规律及暴雨日变化特征,并通过统计对比分析,评估了小时和日值降水数据对暴雨日数及暴雨日变化的影响。结果表明:(1)1961—2018年,河南省年平均暴雨日数变化呈显著上升趋势,速率为0.34 d·(10 a)^(-1);94.6%的气象站点年平均暴雨日数的气候倾向率为正,显著增加的站点数占总站数的73.0%;年平均暴雨日数为1.4 d,以7月最多,平均值为0.52 d,占年平均暴雨日数的37.1%。(2)1961—2018年,河南省暴雨总降水的日变化呈现单峰结构,主峰值出现在05时,为4.5 mm;暴雨日变化峰值呈波动下降趋势,速率为0.16 mm·(10 a)^(-1);日变化峰值大部分出现在00—08时,共42个年份,占79.3%;从空间分布看,各气象站点的暴雨日变化峰值大都出现在00—06时,占总站数的73.5%。(3)2014—2018年,河南省小时和日值降水数据吻合度为100%;吻合暴雨总降水的日变化的主峰值和次峰值出现在04时和17时,分别为4.3 mm和3.4 mm;日变化峰值出现在13—18时的气象站点最多,占总站数的33.9%。(4)小时和日值数据对暴雨日数及暴雨日变化造成了影响,主要表现在年平均暴雨日数及其变化趋势不一致和暴雨日变化的差异。

基于TerraClimate数据集的1960—2019年中国干湿气候变化特征

考虑地理地形和下垫面等因素的影响,选用高空间分辨率TerraClimate数据集分析中国干湿气候变化特征具有重要意义。以该数据集1960—2019年的降水和蒸散月度数据为基础,基于降水量和湿润指数对中国各干湿区范围、干湿气候界线以气候变化趋势进行了分析。结果表明:前后30 a相比,基于两种指标划分的干旱区和湿润区面积负增长,半干旱区和半湿润区面积正增长;各区分界线主要在黑龙江、内蒙古中部和东北部、淮河以及黑龙江等地按年代波动;近60 a来,我国的降水量和湿润指数变化趋势不显著,均在西部和东南部趋于增大,气候变湿,在中部和东北部趋于减小,气候变干。与利用气象站点观测资料的研究结果进行对比评价,TerraClimate数据集能够很好反映中国区域的干湿状况及其变化特征,且降水数据的适用性更为突出。

MSWEP对江苏省太湖流域日降水量和极值降水量的表征能力解析

基于1979—2019年江苏省太湖流域雨量站实测降水资料,综合解析了全球性降水资料MSWEP(multi-source weighted-ensemble precipitation)对日降水量和各历时极值降水量的表征能力。结果表明:MSWEP对研究区日降水量总体上有较强的解释能力,但在山丘区较集中的湖西区和以水域为主的太湖区两个水利分区的精度相对较差,同时MSWEP难以捕捉到暴雨等级的日降水事件;MSWEP可大致反映研究区各历时极值降水量的空间分布格局,但在细节特征上与地面降水场仍有一定差异;随着历时增加,MSWEP极值降水量的精度不断提高,MSWEP对15 d及更长历时极值降水量具有较强的定量表征能力;MSWEP日降水量和极值降水量的误差与降水强度之间均有显著的线性负相关性,存在较明显的“低值高估、高值低估”现象,且MSWEP的精度与空间尺度有关;在研究区面平均尺度上,由于降水量的空间均化效应和尺度不匹配性的消除,MSWEP对日降水量和极值降水量的表征能力明显优于栅格尺度。

Development of China homogenized monthly precipitation dataset during 1900-2009

Based on the collection and processing of the China national-wide monthly station observational precipitation data in 1900-2009, the data series for each station has been tested for their homogeneity with the Standard Normalized Homogeneity Test （SNHT） method and the inhomogeneous parts of the series are adjusted or corrected. Based on the data, the precipitation anomalies during 1900-2009 and the climatology normals during 1971-2000 have been transformed into the grid boxes at 5°×5° and 2°×2° resolutions respectively. And two grid form datasets are constructed by combining the normal and anomalies. After that, the missing values for the 5°×5° grid dataset are interpolated by Empirical Orthogonal Function （EOF） techniques. With the datasets of different resolutions, the precipitation change series during 1900-2009 over China's Mainland are built, and the annual and seasonal precipitation trends for the recent 110 years are analyzed. The result indicates that the annual precipitation shows a slight dryer trend during the past 110 years, notwithstanding lack of statistical confidence. It is worth noting that after the interpolation of the missing values, the annual precipitation amounts in the early 1900s become less, which increases the changing trend of the annual precipitation in China for the whole 110 years slightly （from -7.48 mm/100a to -6.48 mm/100a）.

Development of a Self-Recording Per-Minute Precipitation Dataset for China

The establishment of self-recording precipitation observation systems in China began in 1951,and strips of self-recording precipitation graph paper have been archived since then.More than 9 million sheets of self-recording graph paper from 2253 stations in 31 provinces have been digitized by using image scanning and curve extraction technology.Format specification and quality control have been applied to the digitized data,and the China Surface Self-Recording Per-Minute Precipitation Dataset(V1.0)has been developed.The integrity and accuracy of this dataset are evaluated.This is the first attempt in China to establish a per-minute precipitation dataset that covers the period from1951 to present.Preliminary evaluation reveals that the station density is high and the data continuity is good in most areas of China.However,the integrity of stations in some areas of western China is relatively poor.The availability rate and accuracy rate in summer are higher than 99%at most stations,with the overall availability and accuracy rates reaching as much as 99.42%and 99.22%,respectively.

Estimation of areal precipitation in the Qilian Mountains based on a gridded dataset since 1961

Based on a 0.5°×0.5° daily gridded precipitation dataset and observations in mete- orological stations released by the National Meteorological Information Center, the interan- nual variation of areal precipitation in the Qilian Mountains during 1961-2012 is investigated using principal component analysis （PCA） and regression analysis, and the relationship be- tween areal precipitation and drought accumulation intensity is also analyzed. The results indicate that the spatial distribution of precipitation in the Qilian Mountains can be well re- flected by the gridded dataset. The gridded data-based precipitation in mountainous region is generally larger than that in plain region, and the eastern section of the mountain range usu- ally has more precipitation than the western section. The annual mean areal precipitation in the Qilian Mountains is 724.9×108 m3, and the seasonal means in spring, summer, autumn and winter are 118.9×108 m3, 469.4×108 m3, 122.5×108 m3 and 14.1×108 m3, respectively. Summer is a season with the largest areal precipitation among the four seasons, and the proportion in summer is approximately 64.76%. The areal precipitation in summer, autumn and winter shows increasing trends, but a decreasing trend is seen in spring. Among the four seasons, summer have the largest trend magnitude of 1.7×108 m3-a-1. The correlation be- tween areal precipitation in the mountainous region and dry-wet conditions in the mountains and the surroundings can be well exhibited. There is a negative correlation between drought accumulation intensity and the larger areal precipitation is consistent with the weaker drought intensity for this region.

QpefBD:A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological sciences.Data-driven machine learning,especially deep learning,provides a new technical approach for the quantitative estimation and forecasting of precipitation.A high-quality,large-sample,and labeled training dataset is critical for the successful application of machine-learning technology to a specific field.The present study develops a benchmark dataset that can be applied to machine learning for minutescale quantitative precipitation estimation and forecasting(QpefBD),containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October 2016-2018.Each individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic conditions.Two data labels,i.e.,ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals,are also included.The present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and forecasting.Based on these evaluation metrics,some simple and commonly used methods are applied to evaluate precipitation estimates and forecasts.The results can serve as the benchmark reference for the performance evaluation of machine learning models using this dataset.This paper also gives some suggestions and scenarios of the QpefBD application.We believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences,providing a new way for the identification and forecast of heavy precipitation.

基于日值和小时降水数据诊断中国暴雨时空变化差异的研究(1991-2010)

日值和小时观测降水数据在反映中国暴雨时空格局方面的差异还未有明确结论,因此,亟需通过不同时间分辨率的降水观测数据来深入研究中国暴雨的时空变化特征。该文采用观测的长时间序列(1991-2010年)的小时降水数据和日值降水数据对中国暴雨雨量和暴雨雨日的年际和年代际时空变化特征进行研究。结果表明,在时间变化上,1991-2010年期间小时降水数据和日值降水数据计算的暴雨雨量和暴雨雨日在逐年变化上没有本质上的差异,暴雨雨量和暴雨雨日的年累计值在波动中呈现出明显的增加趋势,但日值降水数据所反映的暴雨雨量和暴雨雨日的波动要大于小时降水数据。在空间格局上,小时降水数据和日值降水数据所反映的中国暴雨雨量和暴雨雨日的空间分布格局没有较大差别,但小时降雨数据反映的更详细,空间分布格局的破碎程度更高。在年代际小时暴雨和日值暴雨两者之间的对比方面,小时降水数据和日值降水数据有较大差异,小时降水数据反映的暴雨雨量和暴雨雨日在长江流域偏少,而在其它地区有不同程度的偏高。在小时暴雨和日值暴雨的各自年代际变化方面,小时降水数据和日值降水数据反映的年代际暴雨空间变化表现出相同的变化趋势,但小时降水数据反映的空间分布特点更加破碎。因此,小时降水数据和日值降水数据在反映中国年代际暴雨时空高低分布方面具有一致性,但量级在不同地区具有不同程度的差异,且在揭示中国年代际暴雨变动方面具有一致性,应根据研究目的选择适当数据。