Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate related diseases and public health. Numerical Weather Models (NWM) are an important component of this work, due to their complete spatial coverage, high resolution, and ability to forecast into the future. In this study, we seek to evaluate the spatiotemporal skill of short-term rainfall forecasts of rainfall across Africa. Specifically, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in-situ observations and are commonly used in analysis of African rainfall. We found that the model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter-tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at one week aggregation time are improved against daily forecasts.
Low-latitude rainfall variability on the daily to intraseasonal timescale is often related to tropical waves, including convectively coupled equatorial waves, the Madden-Julian Oscillation (MJO), and tropical disturbances. Despite the importance of rainfall variability for vulnerable societies in tropical Africa, the relative influence of tropical waves for this region is largely unknown. This article presents the first systematic comparison of the impact of six wave types on precipitation over northern tropical Africa during the transition and full monsoon seasons, using two satellite product…
High quality Quantitative Precipitation Estimation at high spatiotemporal resolution is crucial to many hydrologic/hydro-meteorological designs. Optimal Quantitative Precipitation Estimation of rainfall improves the accuracy of river and flash flood forecasts. In this study, we aim to merge multiple rainfall estimates including rain gauge, radar, Inverse Distance Weighting, Ordinary Co-Kriging, and Adaptive Conditional Bias Penalized Co-Kriging through two most common model selection techniques known as Least Absolute Shrinkage and Selection Operator and Bayesian Model Averaging. The methods w…
Coastally associated rainfall is a common feature especially in tropical and subtropical regions. However, it has been difficult to quantify the contribution of coastal rainfall features to the overall local rainfall. We develop a novel technique to objectively identify precipitation associated with land-sea interaction and apply it to satellite based rainfall estimates. The Maritime Continent, the Bight of Panama, Madagascar and the Mediterranean are found to be regions where land-sea interactions plays a crucial role in the formation of precipitation. In these regions $\approx$ 40% to 60% of…
Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere-ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring one maximum in the monthly rain distribution, it is related to the duration of the wet season. The DSI combines the rainfall intensity with its de…
Monthly rainfall data from June to October for 39 years was used to generate Standardized Precipitation Index (SPI) values based on Gamma distribution for a low rainfall and a high rainfall district of Andhra Pradesh state, India. Comparison of SPI, with actual rainfall and rainfall deviation from the mean indicated that SPI values under-estimate the intensity of dryness/wetness when the rainfall is very low/very high respectively. As a result, the SPI in the worst drought years of 2002 and 2006 in the low rainfall district has indicated only moderate dryness instead of extreme dryness. The ra…
Three-Dimensional Variational Data Assimilation with Rapid Update Cycling for Short-Range Precipitation Forecasting: A Case Study of Heavy Rainfall in Bali, Indonesia
Bandung Institute of Technology (ITB)（万隆理工学院）；Ronin Institute for Independent Scholarship（龙人独立学术研究学院）；School of Systems Science and Industrial Engineering, State University of New York (SUNY)（纽约州立大学系统科学与工业工程学院）
In 2013, the US National Oceanographic and Atmospheric Agency refined the historical rainfall estimates over the African Continent and produced the African Rainfall Climate version 2.0 (ARC2) estimator. ARC2 offers a nearly complete record of daily rainfall estimates since 1983 at 0.1 x 0.1 degree resolution. Despite short-term anomalies in twice-yearly rainy season intensities in Uganda, we identify an overall decrease in average rainfall of about 12% during the past 34 years. Spatiotemporally, these decreases are greatest in agricultural regions of central and western Uganda, but are also re…
The correlation between increased Sahel rainfall and reduced Saharan surface pressure is well established in observations and global climate models, and has been used to imply that increased Sahel rainfall is caused by a stronger shallow meridional circulation (SMC) over the Sahara. This study uses two atmospheric reanalyses to examine interannual variability of Sahel rainfall and the Saharan SMC, which consists of northward near-surface flow across the Sahel into the Sahara and southward flow near 700 hPa out of the Sahara. During wet Sahel years, the Saharan SMC shifts poleward, producing a …
Increasing greenhouse gases will change many aspects of the Earth's climate, from its annual mean to the frequency of extremes such as heat waves and droughts. Here we report that the current generation of climate models predicts a delay in the seasonal cycle of global rainfall and ocean temperature in response to increasing greenhouse gases, with important implications for the regional monsoons. In particular, the rainy season of the semi-arid African Sahel is projected to start later and become shorter: an undesirable change for local rainfed agriculture and pastoralism. Previous work has hi…
Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
This study presents the first systematic comparison of the (thermo-)dynamics associated with all major tropical wave types causing rainfall modulation over northern tropical Africa: Madden Julian Oscillation (MJO), Equatorial Rossby waves (ERs), mixed Rossby-gravity waves (MRGs), Kelvin waves, tropical disturbances (TDs, including African Easterly Waves), and eastward inertio-gravity waves (EIGs). Reanalysis and radiosonde data were analyzed for the period 1981--2013 based on space-time filtering of outgoing longwave radiation. The identified circulation patterns are largely consistent with th…
This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology relies on a regularization technique and makes use of two joint dictionaries of coincidental rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighborhood classification r…
Boundary-layer scheme selection outweighs microphysics in WRF simulations of the August 2018 Kerala extreme rainfall event
Indian Institute of Technology Bhubaneswar（印度布巴内斯瓦尔理工学院）；Indian Institute of Technology Bombay（印度孟买理工学院）；Cochin University of Science and Technology（科钦科技大学）
Egypt faced heavy rainfall starting from 26th October 2016 which caused flooding in Upper Egypt and Red Sea Coast especially the city of Ras Gharib. In this work, Weather Research and Forecasting (WRF) model was used with one domain of 10-km resolution for three days starting from 26th October 2016 to simulate this severe rainy event in terms of precipitation, temperature, pressure and wind speed. In order to use the WRF model several schemes should be configured, one of them is the microphysics scheme. Microphysics is the process by which moisture is removed from the air, based on other therm…
Improved rainfall forecasts in daily use over East Africa
University of Oxford（牛津大学）；National Centre for Atmospheric Science, Oxford, UK（英国牛津国家大气科学中心）；European Centre for Medium-Range Weather Forecasts, Reading, UK（英国雷丁欧洲中期天气预报中心）；IGAD Climate Prediction and Applications Centre, IGAD, Nairobi, Kenya（肯尼亚内罗毕伊加特气候预测与应用中心）；Kenya Meteorological Service Authority, Government of Kenya, Nairobi, Kenya（肯尼亚政府肯尼亚气象局）
Natural disasters caused by heavy rainfall often cost huge loss of life and property. To avoid it, the task of precipitation nowcasting is imminent. To solve the problem, increasingly deep learning methods are proposed to forecast future radar echo images and then the predicted maps have converted the distribution of rainfall. The prevailing spatiotemporal sequence prediction methods apply ConvRNN structure which combines the Convolution and Recurrent neural network. Although improvements based on ConvRNN achieve remarkable success, these methods ignore capturing both local and global spatial …
Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, we analyze the performance of nine operational global ensemble prediction systems (EPSs) relative to climatology-based forecasts for 1 to 5-day accumulated precipitation based on the monsoon seasons 2007-2014 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, we apply state-of-the-art statistical postprocessing methods in form of Bayesian Model Averaging (BMA) and…
With a rapid increase in the number of geostationary satellites around the earth's orbit, there has been a renewed interest in using Global Positioning System (GPS) to understand several phenomenon in earth's atmosphere. Such study using GPS devices are popular amongst the remote sensing community, as they provide several advantages with respect to scalability and range of applications. In this paper, we discuss how GPS signals can be used to estimate the amount of water vapor in the atmosphere. Furthermore, we demonstrate the importance of such precipitable water vapor (PWV) in the atmosphere…
In this diagnostic study we analyze changes of rainfall seasonality and dry spells by the end of the twenty-first century under the most extreme IPCC5 emission scenario (RCP8.5) as projected by twenty-four coupled climate models participating to Coupled Model Intercomparison Project 5. We use estimates of the centroid of the monthly rainfall distribution as an index of the rainfall timing and a threshold-independent, information theory-based quantity such as relative entropy (RE) to quantify the concentration of annual rainfall and the number of dry months and to build a monsoon dimensionless …
This paper presents an algorithm that relies on a series of dense and deep neural networks for passive microwave retrieval of precipitation. The neural networks learn from coincidences of brightness temperatures from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) with the active precipitating retrievals from the Dual-frequency Precipitation Radar (DPR) onboard GPM as well as those from the {CloudSat} Profiling Radar (CPR). The algorithm first detects the precipitation occurrence and phase and then estimates its rate, while conditioning the results to some key ancillary infor…
Evaluation of High-Resolution Gridded Precipitation Datasets Against a Dense Rain Gauge Network During the Indian Summer Monsoon
Indian Institute of Technology (IIT) Madras（印度理工学院马德拉斯分校）；CSIR National Institute of Data Science and Artificial Intelligence（印度科学、技术和医学研究理事会数据科学与人工智能国家研究所）；IIT Delhi（印度理工学院德里分校）
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa
Laboratoire d’Océanographie et des Sciences de l’Environnement et du Climat (LOSEC)Université Assane Seck de Ziguinchor（西古尔省阿萨内·塞克大学海洋与环境气候学实验室）；Institut Pierre-Simon Laplace (IPSL), Sorbonne Université, IRD（皮埃尔-西蒙·拉普拉斯研究所，索邦大学，法国国家科学研究中心）
Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP) models still have modest skill after a few days. Here we use a ConvLSTM network to develop a deep learning model for precipitation forecasting. The crux of the idea is to develop a forecasting model which involves convolution based feature selection and uses long term memory in the meteorological fields in conjunction with gradient based learning algorithm. P…
This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long- and short-term memory (LSTM) network is selected. Daily precipitation and mean air temperature were used as model input to estimate daily flow discharge. After model training and verification, two experimental simulations were conducted with hypothetical inputs instead of observed meteorological data to clarify the response of the trained model to the inputs. The first numerical experime…
For precipitation retrievals over land, using satellite measurements in microwave bands, it is important to properly discriminate the weak rainfall signals from strong and highly variable background surface emission. Traditionally, land rainfall retrieval methods often rely on a weak signal of rainfall scattering on high-frequency channels (85 GHz) and make use of empirical thresholding and regression-based techniques. Due to the increased ground surface signal interference, precipitation retrieval over radiometrically complex land surfaces, especially over snow-covered lands, deserts and coas…
Northern Chile is one of the most arid regions in the world, with precipitation mainly occurring during austral summer, between December and April 1966-2015. The aim of this study is to classify the main weather regimes derived from sea level pressure, surface wind speed, 500 or 250 hPa geopotential heights, in order to measure their influence on precipitation anomalies and determine if they can be considered sources of predictability of rainfall in this region. Four weather regimes were found to optimally describe atmospheric circulation in the study area and for each of the four levels descr…
PrecipCorrDiff: A Deep Learning Framework for Generating 0.0625$^\circ$ High-Resolution IMD Gridded Precipitation Data
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Government of India（印度热带气象研究所）；Vishwakarma Institute of Technology（维斯瓦卡尔马理工学院）；India Meteorological Department, Ministry of Earth Sciences, Government of India（印度气象局）；NVIDIA Graphics India Pvt. Ltd.（英伟达图形印度私人有限公司）
How Effective Are Time-Series Models for Precipitation Nowcasting? A Comprehensive Benchmark for GNSS-based Precipitation Nowcasting
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科技创新园）；Wuhan University of Technology（武汉理工大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；Interdisciplinary Artificial Intelligence Research Institute, Wuhan College（武汉学院交叉人工智能研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai（上海人工智能实验室）；School of Earth and Space Science and Technology, Wuhan University（武汉大学地球空间科学与技术学院）；School of Water Resources and Hydropower Engineering, Wuhan University（武汉大学水利水电学院）；GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）
Two Hebrew folk meteorological proverbs tested: rainfall on Rosh Chodesh and Shabbat Mevarechim as predictors of monthly precipitation (Israel, 1950-2024)
We carry out a study of the statistical distribution of rainfall precipitation data for 20 cites in India. We have determined the best-fit probability distribution for these cities from the monthly precipitation data spanning 100 years of observations from 1901 to 2002. To fit the observed data, we considered 10 different distributions. The efficacy of the fits for these distributions was evaluated using four empirical non-parametric goodness-of-fit tests namely Kolmogorov-Smirnov, Anderson-Darling, Chi-Square, Akaike information criterion, and Bayesian Information criterion. Finally, the best…
Daily rainfall extremes and annual totals have increased in large parts of the global land area over the last decades. These observations are consistent with theoretical considerations of a warming climate. However, until recently these global tendencies have not been shown to consistently affect land regions with limited moisture availability. A recent study, published by Donat et al. (2016, Nature Climate Change, doi:10.1038/nclimate2941), now identified rapid increases in globally aggregated dry region daily extreme rainfall and annual rainfall totals. Here, we reassess the respective analy…
In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. Whil…
Recent research has community have shown that tropical convection and rainfall is sensitive to mid-tropospheric humidity. Therefore it has been suggested to improve the representation of moist convection by making cumulus parameterizations more sensitive to mid-tropospheric moisture. Climate models show considerable rainfall biases in coastal tropical areas, where approx. 33 % of the overall rainfall received is associated with coastal land-sea interaction. Building on an algorithm to objectively identify rainfall that is associated with land-sea interaction we investigate whether the relation…
Daily precipitation time series are composed of null entries corresponding to dry days and nonzero entries that describe the rainfall amounts on wet days. Assuming that wet days follow a Bernoulli process with success probability $p$, we show that the presence of dry days induces negative correlations between record-breaking precipitation events. The resulting non-monotonic behavior of the Fano factor of the record counting process is recovered in empirical data. We derive the full probability distribution $P(R,n)$ of the number of records $R_n$ up to time $n$, and show that for large $n$, its…
In order to study the relation between the components of atmospheric circulation and summer precipitation over the Iranian plateau, daily rainfall data of synoptic stations of Iran were obtained from IRIMO for the period 1970 to 2003. Composite maps of different components such as pressure levels, vector wind, U and V winds, vertical velocity, Specific humidity, OLR, streamlines, vorticity, and the temperature was drawn and analyzed. The results showed that the summer circulation of the atmosphere over South West Asia after a sudden change from late May to early June was established in its nor…
The sub-seasonal and synoptic-scale variability of the Indian summer monsoon rainfall are controlled primarily by monsoon intra-seasonal oscillations (MISO) and low pressure systems (LPS), respectively. The positive and negative phases of MISO lead to alternate epochs of above-normal (active) and below-normal (break) spells of rainfall. LPSs are embedded within the different phases of MISO and are known to produce heavy precipitation events over central India. Whether the interaction with the MISO phases modulates the precipitation response of LPSs, and thereby the characteristics of extreme r…
Indian Institute of Tropical Meteorology（印度热带气象研究所）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Technology Bombay（孟买印度理工学院）；Queen Mary University of London（伦敦玛丽女王大学）
The formation of precipitation in state-of-the-art weather and climate models is an important process. The understanding of its relationship with other variables can lead to endless benefits, particularly for the world's monsoon regions dependent on rainfall as a support for livelihood. Various factors play a crucial role in the formation of rainfall, and those physical processes are leading to significant biases in the operational weather forecasts. We use the UNET architecture of a deep convolutional neural network with residual learning as a proof of concept to learn global data-driven mode…
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
Institute for Applied Mathematics and Information Technologies, CNR（意大利国家研究委员会应用数学与信息技术研究所）；Institute of Atmospheric Sciences and Climate, CNR（意大利国家研究委员会大气科学与气候研究所）；Politecnico di Torino（都灵理工大学）
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所，地球科学部）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Ramrao Adik Institute of Technology（拉姆劳·阿迪克技术学院）；Defence Institute of Advanced Technology（国防高级技术研究所）
Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to non-linear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL…
Global spatio-temporal downscaling of ERA5 precipitation through generative AI
Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU)（气象与气候研究所——大气环境研究（IMK-IFU））；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing (IMK-ASF)（气象与气候研究所——大气痕量气体与遥感（IMK-ASF））；University of Augsburg（奥格斯堡大学）
