Large-scale precipitation estimation using Kalpana-1 IR measurements and its validation using GPCP and GPCC data

Large-scale precipitation estimation is very important for climate science because precipitation is a major component of the earth's water and energy cycles. In the present study, the GOES precipitation index technique has been applied to the Kalpana-1 satellite infrared (IR) images of every three-hourly, i.e., of 0000, 0300, 0600,??., 2100?hours UTC, for rainfall estimation as a preparatory to the INSAT-3D. After the temperatures of all the pixels in a grid are known, they are distributed to generate a three-hourly 24-class histogram of brightness temperatures of IR (10.5?C12.5???m) images for a 1.0°?×?1.0° latitude/longitude box. The daily, monthly, and seasonal rainfall have been estimated using these three-hourly rain estimates for the entire south-west monsoon period of 2009 in the present study. To investigate the potential of these rainfall estimates, the validation of monthly and seasonal rainfall estimates has been carried out using the Global Precipitation Climatology Project and Global Precipitation Climatology Centre data. The validation results show that the present technique works very well for the large-scale precipitation estimation qualitatively as well as quantitatively. The results also suggest that the simple IR-based estimation technique can be used to estimate rainfall for tropical areas at a larger temporal scale for climatological applications.     

A precipitation regionalization and regime for Iran based on multivariate analysis

Theoretical and Applied Climatology - Monthly precipitation time series of 155 synoptic stations distributed over Iran, covering 1990–2014 time period, were used to identify areas with...     

Homogeneity analysis of precipitation series in Iran

Assessment of the reliability and quality of historical precipitation data is required in the modeling of hydrology and water resource processes and for climate change studies. The homogeneity of the annual and monthly precipitation data sets throughout Iran was tested using the Bayesian, Cumulative Deviations, and von Neumann tests at a significance level of 0.05. The precipitation records from 41 meteorological stations covering the years between 1966 and 2005 were considered. The annual series of Iranian precipitation were found to be homogeneous by applying the Bayesian and Cumulative Deviations tests, while the von Neumann test detected inhomogeneities at seven stations. Almost all the monthly precipitation data sets are homogeneous and considered as “useful.” The outputs of the statistical tests for the homogeneity analysis of the precipitation time series had discrepancies in some cases which are related to different sensitivities of the tests to break in the time series. It was found that the von Neumann test is more sensitive than the Bayesian and Cumulative Deviations tests in the determination of inhomogeneity in the precipitation series.     

Simulating forest dynamics in a complex topography using gridded climatic data

The forest model ForClim was used to evaluate the applicability of gap models in complex topography when the climatic input data is provided by a global database of 0.5° resolution. The analysis was based on 12 grid cells along an altitudinal gradient in the European Alps. Forest dynamics were studied both under current climate as well as under four prescribed 2 × CO₂ scenarios of climatic change obtained from General Circulation Models, which allowed to assess the sensitivity of mountainous forests to climatic change.Under current climate, ForClim produces plausible patterns of species composition in space and time, although the results for single grid cells sometimes are not representative of reality due to the limited precision of the climatic input data.Under the scenarios of climatic change, three responses of the vegetation are observed, i.e., afforestation, gradual changes of the species composition, and dieback of today's forest. In some cases widely differing species compositions are obtained depending on the climate scenario used, suggesting that mountainous forests are quite sensitive to climatic change. Some of the new forests have analogs on the modern landscape, but in other cases non-analog communities are formed, pointing at the importance of the individualistic response of species to climate.The applicability of gap models on a regular grid in a complex topography is discussed. It is concluded that for their application on a continental scale, it would be desirable to replace the species in the models by plant functional types. It is suggested that simulation studies like the present one must not be interpreted as predictions of the future fate of forests, but as means to assess their sensitivity to climatic change.     

Spatial autocorrelation analysis of extreme precipitation in Iran

Spatial variations in extreme precipitation events make hydrological, climatological, social, environmental and agricultural effects on a country. This study presents the spatiotemporal autocorrelation analysis of extreme precipitation events over Iran using gridded data on daily precipitation for the period 1961–2010. The 95th percentile is considered as extreme precipitation factor. The spatial autocorrelation of extreme precipitation is examined by three commonly used spatial autocorrelation statistics, the G _i statistic index, Moran’s I global index, and Local Moran’s I (LISA) index, at the 95 and 99% significant confidence level. The results showed a strong significant spatial autocorrelation for extreme precipitation events with the highest Moran’s I value in January. The positive significant autocorrelation of extreme precipitation is observed over the southern parts of the Caspian Sea and Zagros Mountains ranges, while the negative significant autocorrelation is observed over the central and eastern parts of country. In spring and summer the positive autocorrelation cores displace from the Zagros Mountains ranges to the northwestern and southeastern parts.     

Observed and projected changes in temperature and precipitation extremes based on CORDEX data over Iran

Theoretical and Applied Climatology - Due to increasing greenhouse gases, Iran is experiencing changes in patterns and trends of extreme climate events. Future climate extremes are one of the...     

Precipitation trends using in-situ and gridded datasets

Six in situ precipitation time series of varying time periods in the northwestern region and the Global Precipitation Climatology Centre (GPCC) v6 0.5° monthly dataset (1901–2010) were statistically examined for monotonic trends in Trinidad. The Pettit test was used to investigate the abrupt changes in the mean while the Mann–Kendall test was employed to assess the monotonic trends. It was found that three in situ stations and the six grids experienced abrupt changes in the rainfall patterns and that there was an apparent shift in the seasons. In addition, for five out of the six in situ stations no monotonic change was detected in the monthly, seasonal, and annual rainfall patterns. Gradual decreases were detected in the calculated weighted area average for five stations, the GPCCv6 dataset and St. Ann’s time series. The GPCCv6 data indicated that the dry season in the southern Trinidad is becoming drier. Results also suggested that the range between the greatest and lowest recorded rainfall values for some months have increased while others decreased. The gridded dataset appears to give a good representation of the dry season (January to May) rainfall compared with the wet season (June to December) and was found to be negatively biased for the north-western region but may not necessarily be so for the entire island. The results suggested that in the north-western region mirco-climates may exist. It is recommended that further investigations are needed using in situ data.     

Comparison of projection skills of deterministic ensemble methods using pseudo-simulation data generated from multivariate Gaussian distribution

The projection skills of five ensemble methods were analyzed according to simulation skills, training period, and ensemble members, using 198 sets of pseudo-simulation data (PSD) produced by random number generation assuming the simulated temperature of regional climate models. The PSD sets were classified into 18 categories according to the relative magnitude of bias, variance ratio, and correlation coefficient, where each category had 11 sets (including 1 truth set) with 50 samples. The ensemble methods used were as follows: equal weighted averaging without bias correction (EWA_NBC), EWA with bias correction (EWA_WBC), weighted ensemble averaging based on root mean square errors and correlation (WEA_RAC), WEA based on the Taylor score (WEA_Tay), and multivariate linear regression (Mul_Reg). The projection skills of the ensemble methods improved generally as compared with the best member for each category. However, their projection skills are significantly affected by the simulation skills of the ensemble member. The weighted ensemble methods showed better projection skills than non-weighted methods, in particular, for the PSD categories having systematic biases and various correlation coefficients. The EWA_NBC showed considerably lower projection skills than the other methods, in particular, for the PSD categories with systematic biases. Although Mul_Reg showed relatively good skills, it showed strong sensitivity to the PSD categories, training periods, and number of members. On the other hand, the WEA_Tay and WEA_RAC showed relatively superior skills in both the accuracy and reliability for all the sensitivity experiments. This indicates that WEA_Tay and WEA_RAC are applicable even for simulation data with systematic biases, a short training period, and a small number of ensemble members.     

多模式降水资料在贵州的检验分析

本文检验了2020年3月至2021年2月ECMWF和GRAPES以及中央台格点产品（以下简称SCMOC）和省台格点产品（以下简称SPCC）4家降水预报产品逐24h未来5天在贵州的预报质量,结论如下：（1）ECMWF和SCMOC与实况的相关系数最高,SCMOC和SPCC预报降水的变化幅度较观测偏大,而GRAPES预报降水的变化幅度较观测则是明显偏小。（2）SCMOC的晴雨准确率最高,除在72h预报时效SPCC的准确率略高于SCMOC外,其余预报时效SPCC准确率均低于SCMOC,表明SPCC的订正能力需要进一步提升。（3）在小雨量级,4种降水预报产品的TS评分相差不大,ECMWF和GRAPES的ETS评分明显低于SCMOC和SPCC,其中GRAPES的TS评分在5个预报时效内均高于ECMWF。在中雨量级,前3个预报时效内ECMWF的TS和ETS评分均高于其他三家,ECMWF在5个预报时效内预报有降水的次数大于实况出现的降水次数,但空报次数并不是最多的,在后2个预报时效内,SCMOC的TS和ETS评分均是最高的,但与其他家相差不大。在大雨量级,24h和96h预报时效ECMWF的TS和ETS评分均是最高的,而在48h、72h、120h预报时效SCMOC的TS和ETS评分是最高的。在暴雨及以上量级,前3个时效内SPCC 的TS和ETS评分均是最高,且48h的TS评分空间分布也是最优的,表明SPCC对暴雨及以上量级在前3个预报时效内订正能力较好。     

TOVS资料在降水分析预报中的应用

利用两年多的夏季TOVS部分反演产品分析了黑龙江省降水，认为它反映小范围阵性降水的能力比较强，而对区域性强降水的反映能力弱一些，并初步探讨了出现实种情况的成因。     

Spatiotemporal analysis of precipitation and temperature concentration using PCI and TCI: a case study of Khuzestan Province,Iran

Theoretical and Applied Climatology - Temperature and precipitation are the basic elements of climate, and their variation can change the water demands of different uses. In this study, the trend...     

Spatial patterns and temporal trends of precipitation in Iran

Spatial patterns of monthly, seasonal and annual precipitation over Iran and the corresponding long-term trends for the period 1951–2009 are investigated using the Global Precipitation Climatology Centre gridded dataset. Results suggest that the spatial patterns of annual, winter and spring precipitation and the associated coefficients of variation reflect the role of orography and latitudinal extent between central-southern arid and semi-arid regions and northern and western mountainous areas. It is also shown that precipitation occurrence is almost regularly distributed within the year in northern areas while it is more concentrated in a few months in southern Iran. The spatial distribution of Mann–Kendal trend test (Z statistics) for annual precipitation showed downward trend in north-western and south-eastern Iran, whereas western, central and north-eastern exhibited upward trend, though not statistically significant in most regions. Results for winter and autumn revealed upward trend in most parts of the country, with the exception of north-western and south-eastern where a downward trend is observed; in spring and summer, a downward trend seems to prevail in most of Iran. However, for all seasons the areas where the detected trend is statistically significant are limited to a few spot regions. The overall results suggest that the precipitation is decreasing in spring and summer and increasing in autumn and winter in most of Iran, i.e. less precipitation during the warm season with a consequent intensification of seasonality and dryness of the country. However, since the detected trends are often not statistically significant, any stringent conclusion cannot be done on the future tendencies.     

Structure of chemical elements deposited with atmospheric precipitation in the north of the European territory of Russia derived with multivariate analysis methods

Data on deposition density of Ag, Al, As, B, Ba, Bi, Br, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sb, Si, Sn, Sr, Th, Tl, U, V, Zn, Cl^?, NO ₃ ^? , and SO4 ₄ ^2? in atmospheric precipitation were derived at 11 observation stations in the northern part of the European territory of Russia (ETR). Three sets (factors) of chemical indices that are mutually independent and determine the precipitation chemical content over that territory were revealed by using the hierarchical factor analysis of deposited density of chemical ingredients studied. These are two first-order factors: one is presented with the elements of a “marine” origin; the second is associated with biogenic elements. The second-order factor is formed by the ingredients characteristic of the combustion products of different fossil fuels. The structure and space-time distribution of factors are considered. The discriminant and cluster analyses allowed to classify the observation stations considered by the atmospheric precipitation chemistry and to show that Amderma, Vorkuta, Arkhangelsk, and Kargopol stations mostly differ from each other.     

Statistical evaluation of the performance of gridded monthly precipitation products from reanalysis data,satellite estimates,and merged analyses over China

Theoretical and Applied Climatology - In this study, we compared the following four different gridded monthly precipitation products: the National Centers for Environmental Prediction version 2...     

Urban and peri-urban precipitation and air temperature trends in mega cities of the world using multiple trend analysis methods

Theoretical and Applied Climatology - Urbanization plays an important role in altering local to regional climate. In this study, the trends in precipitation and the air temperature were...     

Variational data assimilation in the tropics using precipitation data part I: Column model

Summary This paper describes initial effort in the development of a four-dimensional variational data assimilation (4D-Var) in the tropics using precipitation data derived from remote sensing. The method of 4D-Var using precipitation data is formulated, and modifications to the parameterization schemes of moist processes to remove zeroth-order discontinuities are described. Variational data assimilation experiments are carried out using a column model to investigate the problems caused by discontinuities in parameterization schemes and assess the impact of assimilating precipitation data in the tropics.It is found that variational data assimilation with discontinuous parameterization schemes exhibits large fluctuations during the minimization process, slow convergence rates, and large analysis errors. The fluctuations become much more serious when precipitation data is assimilated. Precipitation data is very useful to estimate divergence in the tropics, provided that the temporal resolution of the data is sufficiently high. However, its impact on the analysis of temperature and moisture is not clear in the column model assimilation experiments, possibly due to the absence of horizontal advection.     

Fractal analysis of climatic data: Annual precipitation records in Spain

Summary The rescaled range analysis was applied to the annual precipitation series from 10 weather stations in Spain for the period 1901–1989. The analysis reveals that the series of precipitations fits a fractal distribution, with a mean fractal dimension of 1.32 ± 0.01. This lies in the same order of magnitude as the fractal dimensions obtained from other macrometeorological and paleoclimatic registers. The favourable comparison between fractal dimensions of the variables on both small time scales and long-term time spans suggests that such values are characteristic of climatic change over the spectral range of 10 to 10⁶ years. The results contribute to the establishment of this assumption as a valid hypothesis for the interpolation of climatic change from one scale to the next, and also in applications such as hydrological design.With 2 Figures     

4种再分析月降水数据在内蒙古地区的适用性分析

利用1981—2020年气象台站的实测降水数据,对CRA40、ERA5、JRA55和MERRA2这4种再分析月降水数据在内蒙古地区的降水分布特征,与实测降水的相关性和误差进行分析。结果表明：(1)内蒙古地区4种再分析降水数据的空间分布与台站降水基本一致,误差分析表明CRA40与MERRA2的数据质量较高,ERA5次之,而JRA55数据质量相对较差。(2)CRA40和MERRA2在1983—1986年存在明显的降水低估,ERA5在2005年之后的内蒙古中东部出现明显的降水低估,JRA55在115°E以东存在明显降水高估,在115°E以西则以降水低估为主。(3)4种再分析月降水数据的年内最大均方根误差和绝对误差均集中在6—8月,与台站降水相关系数的年内最小值均出现在7月,内蒙古夏季汛期是再分析降水误差产生的主要时段。