Precipitation Climatology Research Articles

Improvements in tropical precipitation and sea surface air temperature fields in a coupled atmosphere–ocean data assimilation system

AbstractA coupled atmosphere–ocean data assimilation system, the Meteorological Research Institute‐Coupled Data Assimilation System Version 1 (MRI‐CDA1), was developed based on the coupled atmosphere–ocean general circulation model and separate atmosphere and ocean analysis routines operated by the Japan Meteorological Agency (JMA). To implement coupled atmosphere–ocean data assimilation, 6‐hr data assimilation cycles with the coupled model in the outer loop are adopted in atmospheric data assimilation, whereas incremental analysis updates with 10‐day data assimilation cycles are adopted in ocean data assimilation. A coupled data assimilation (reanalysis) experiment (CDA‐Exp) is conducted using MRI‐CDA1 along with an uncoupled reanalysis experiment (UCPL‐Exp) in which the same atmospheric component of the coupled model is forced by prescribed sea surface temperature (SST) similarly to the JMA reanalysis, JRA‐55. Climatological precipitation and variations in precipitation and sea surface air temperature (SAT) are better represented in CDA‐Exp than in JRA‐55, which is brought about by the modification of the atmosphere model physics of the coupled model to better represent climate states. In CDA‐Exp, the SST adjustment to the atmosphere amplifies the lead/lag correlations between subseasonal variations of SST and precipitation. The atmosphere–ocean coupling generates SST variations associated with tropical instability waves, and the SAT field responds to SST variations. The SST–precipitation and SST–SAT relationships on the weather timescale are also recovered in CDA‐Exp, although they are hardly seen in UCPL‐Exp. The coupled model physics generates weather‐timescale SST variations consistent with the atmospheric state, and the atmospheric parameters respond to the SST variations through the coupled model physics. This study suggests that the benefits of coupled data assimilation would be more evident if the ability of MRI‐CDA1 to represent SST variations and its interaction with the atmosphere are improved.

Assessing the impact of land-use and land-cover changes on the climate over India using a regional climate model (RegCM4)

Changes in land use and land cover (LULC) in recent decades are among the factors responsible for recent climate change. We assessed the impact of LULC changes on the climate over India through multi-decade simulations (3 decades) using the Regional Climate Modeling system (RegCM4) with fixed and changed LULC. Differences between the 2 simulations were considered to be impacts of LULC changes on the regional climate. The main focus was on the decadal climatology of temperature, precipitation and several other important climatic variables including specific humidity, moisture content, sensible heat, evapotranspiration over Indian regions and their responses to the LULC changes. We found a decreasing trend in annual precipitation and increasing trend in annual temperature over India during 1981-2010. The LULC changes over northwest, south peninsular and west-central regions of India contributed to warming during 1991-2010, while changes over central-north and northeastern regions resulted in cooling during this period. LULC changes also indicated a wet effect over south peninsular and west-central regions of India and a dry effect over northwest India in 1991-2010. The analysis indicated that LULC changes resulted in decreases in evapotranspiration, moisture content and specific humidity that led to decreased precipitation. On the other hand, changes in LULC also caused significant increases in sensible heat flux and albedo, which led to a temperature rise. Our overall modeling results further our understanding of the processes associated with LULC change feedback to the climate over Indian regions.

The Climatic Analysis of Summer Monsoon Extreme Precipitation Events over West Africa in CMIP6 Simulations

We evaluate the capability of 21 models from the new state-of-the-art Coupled Model Intercomparison Project, Phase 6 (CMIP6) in the representation of present-day precipitation characteristics and extremes along with their statistics in simulating daily precipitation during the West African Monsoon (WAM) period (June–September). The study uses a set of standard extreme precipitation indices as defined by the Expert Team on Climate Change Detection and Indices constructed using CMIP6 models and observational datasets for comparison. Three observations; Global Precipitation Climatology Project (GPCP), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Tropical Applications of Meteorology using SATellite and ground-based observation (TAMSAT) datasets are used for the validation of the model simulations. The results show that observed datasets present nearly the same spatial pattern but discrepancies in the magnitude of rainfall characteristics. The models show substantial discrepancies in comparison with the observations and among themselves. A number of the models depict the pattern of rainfall intensity as observed but some models overestimate the pattern over the coastal parts (FGOALS-f3-L and GFDL-ESM4) and western part (FGOALS-f3-L) of West Africa. All model simulations explicitly show the pattern of wet days but with large discrepancies in their frequencies. On extreme rainfall, half of the models express more intense extremes in the 95th percentiles while the other half simulate less intense extremes. All the models overestimate the mean maximum wet spell length except FGOALS-f3-L. The spatial patterns of the mean maximum dry spell length show a good general agreement across the different models, and the observations except for four models that show an overestimation in the Sahara subregion. INM-CM4-8 and INM-CM5-0 display smaller discrepancies from their long-term average rainfall characteristics, in terms of extreme rainfall estimates than the other CMIP6 datasets. For the frequency of heavy rainfall, TaiESM1 and IPSL-CMGA-LR perform better when compared with observational datasets. MIROC6 and GFDL-ESM4 displayed the largest error in representing the frequency of heavy rainfall and 95th percentile extremes, and therefore, cannot be reliable. The study has assessed how rainfall extremes are captured in both observation and the models. Though there are some discrepancies, it gives room for improvement of the models in the next version of CMIP.

Validación de tres productos de precipitación en la costa ecuatoriana.

Three precipitation products with global coverage have been analyzed for theEcuadorian coastal zone (below 1000 m above sea level), during the period 1964-1994, to evaluate their behavior and consistency, in addition to their response to thegoverning seasonal cycle. The precipitation products are: CRU (Climate ResearchUnit, University of East Anglia), GPCC (Global Precipitation Climatology Centre,Deutscher Wetterdienst), and UDEL (University of Delaware), regridded at a regulargrid of 0.25°. The reference dataset chosen (INMAHI12k) is based on observationsfrom 51 rain-gauges located at meteorological stations on the Ecuadorian littoral,gridded at 0.25°.The analysis is based, principally, on comparison of the products withthe observed annual cycle and total annual precipitation in previously defihomogeneous coastal precipitation zones, using bias, bias/error and correlationanalyses. Additionally, a Kruskal-Wallis test was executed over every grid point toassess the statistical differences between these products and reference dataset.Generally, the three products reproduced consistently the coastal annual cycle,however, from the statistical comparison we conclude that GPCC dataset is the onethat better adjusts to the observed precipitation regime. In addition, although the threeprecipitation datasets used in this study are all based on rain-gauge data from NationalMeteorological and Hydrological Services around the world, the data assimilation andprocessing systems (including the interpolation procedures) could explain thedifferences found between the products.

Assessing the Skills of Rossby Centre Regional Climate Model in Simulating Observed Rainfall over Rwanda

Rainfall over Rwanda is highly variable both in space and time. This variability leads to chronic food insecurity due to the overdependence of the economy on rain-fed agriculture systems. This study aims to evaluate the skills of Rossby Centre Regional Climate Model (RCA4) simulations driven by 10 GCMs for the period 1951-2005 using the Global Precipitation Climatology Centre (GPCC v8) as a reference. Different statistical and geospatial metrics were used to deduce the model’s skills in simulating seasonal and annual rainfall. Results show that the country received bimodal rainfall pattern; March-May (MAM) and September-December (SOND). The RCA4 models are inconsistent in simulating the MAM rainy peak. However, the models are coherent in simulating SOND seasonal peak despite exhibiting wet bias. The models show reasonable skills in simulating mean annual cycle than interannual variability as depicted by insignificant correlation and different signs of rainfall trend. Conclusively, the performance of RCA4 models in simulating observed rainfall characteristics over Rwanda is relatively weak. The performance of the models differs at various time scales. Nevertheless, the models can be ranked from the best performing to the least as; CSIRO, CanESM2, CNRM, GFDL, MIROC5, ENS, EC-Earth, HadGEM2, IPSL, MPI, and NorESM1. Ranking the performance of RCA4 historical models acts as a basis for future climate model’s selection depending on the purpose of the study. The findings of this study may help in devising appropriate climate adaptation measures to respond to the ongoing global warming for sustainable economic and livelihood development. Additionally, modelers may improve the model’s parametrization schemes and lessen the inherent chronic biases for a better presentation of the future.

Performance of the IPCC AR6 models in simulating the relation of the western North Pacific subtropical high to the spring northern tropical Atlantic SST

AbstractThis study examines the relationship between the spring northern tropical Atlantic (NTA) sea surface temperature (SST) and its following summer western North Pacific subtropical high (WNPSH) in historical simulations of 20 coupled models of the IPCC AR6. These models well simulate the spring NTA SST, as well as the WNPSH‐related atmospheric and precipitation anomalies over the Asian monsoon region. Ensemble mean of these models reproduces the observed relation and processes linking the spring NTA SST to the summer WNPSH. In the ensemble mean, spring NTA SST warming persists to the following summer, and induces an anomalous Walker circulation with an ascent over the tropical Atlantic and a descent over the tropical central Pacific. The associated precipitation decrease over the tropical central Pacific induces an anomalous low‐level anticyclone over the WNP via a Rossby wave atmospheric response. Meanwhile, spring NTA SST warming leads to easterly wind anomalies over the Indian Ocean (IO) via a Kelvin wave atmospheric response and causes SST warming there via reducing wind speeds. Then, the precipitation anomalies induced by the IO SST warming result in an enhancement of the WNPSH. There exists a large diversity of the NTA SST‐WNPSH relationship among the models. This diversity is related to the difference in the climatological mean IO precipitation. The models with large mean IO precipitation provide a favourable condition for the IO SST warming to bring large local precipitation anomalies and trigger strong easterly wind anomalies over the tropical WNP, and thus exert a strong impact on the WNPSH.

Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

A new estimate for oceanic precipitation amount and distribution using complementary precipitation observations from space and comparison with GPCP

This study produces near global (81°S/N) spatial and seasonal maps of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference that can be used to assess current precipitation products. The Merged CloudSat, Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) (MCTG) estimate uses light rainfall and snowfall estimates from CloudSat and merges them with the combined radar-radiometer products available from the TRMM and the GPM mission. The merging process is performed at grid level and for each season, so maps of the merged products can be constructed. MCTG was then compared with the most recent Global Precipitation Climatology Project (GPCP) product (V2.3) to identify regional, seasonal, and annual differences between the two products. Several areas of major differences were highlighted, among those are regions near 5°N/S, 20°N/S, 40°N/S and 60°N/S. These regions also show seasonal variations in the magnitude and exact location of the differences. The largest differences between GPCP and MCTG occur around 40°S and 60°S, showing an under- and overestimation of MCTG, respectively. Overall, MCTG suggests that GPCP underestimates the annual oceanic precipitation rate by 9.03%, while seasonal rates are underestimated by 7.14%, 9.71%, 9.96%, and 9.73% for winter, spring, summer, and fall, respectively. Such differences in global oceanic precipitation rates need to be considered in the future updates in water and energy budget calculations and in future updates of GPCP.

Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model

The influence of entropy values obtained under different multiscale permutation entropy scales on the complexity and unconstrained extreme value problem of traditional bionics algorithms was assessed. The multiscale permutation entropy (MSPE) eigenvalue calculation method based on the chaotic moth-flame optimization-based projection pursuit threat target evaluation (CMFO-PPTTE) model is proposed (CMFO-PPTTE-MSPE). The CMFO-PPTTE-MSPE not only solves the problems of rough calculation and information loss in traditional eigenvalue calculation but also improves the shortcomings of slow convergence and local optimality in the intelligent bionic algorithm. For this purpose, the monthly precipitation of 13 administrative regions in Heilongjiang Province under the Global Precipitation Climatology Centre dataset (GPCC) from 1967 to 2017 was evaluated to improve the precipitation complexity entropy accuracy. The main influencing factors were altitude (p < 0.05), water area (p < 0.05), urban construction area (p < 0.05) and forestland area (p < 0.01). Radial basis function (RBF) neural networks were used to forecast the precipitation in Heilongjiang Province over 36 months, and a better forecast was obtained. To verify the rationality of CMFO-PPTTE-MSPE under GPCC data, compared with the situ data, it is found that GPCC data can more accurately classify the complexity grade of Daxing’anling region. At the same time, the administrative discrimination of GPCC data (1.107) is significantly higher than the situ data (1.023). To verify the rationality of the CMFO-PPTTE-MSPE, the partial mean of MSPE (MSPE-PM), whale optimization algorithm PPTTE (WOA-PPTTE) and ω-particle swarm optimization PPTTE (ω-PSO-PPTTE) models were also used to calculate the MSPE eigenvalues under GPCC data. Comparisons and evaluations were performed after dividing the grade based on geographical discrimination (CMFO-PPTTE-MSPE (1.107) > WOA-PPTTE-MSPE (1.094) > W-PSO-PPTTE-MSPE (1.090) > MSPE-PM (1.063)) and administrative discrimination (CMFO-PPTTE-MSPE (1.146) > W-PSO-PPTTE-MSPE (1.002) > MSPE-PM (1.012) > WOA-PPTTE-MSPE (1.002)). The CMFO-PPTTE-MSPE had a significantly higher distinguishing capabilities than the other algorithms and thus could better distinguish the complexity of precipitation in different regions and geographic locations. In summary, the CMFO-PPTTE-MSPE is beneficial for analyzing precipitation complexity and represents a novel approach for mining the fine structural features of regional hydrological series. However, determining how to apply artificial intelligence algorithms to reasonably calibrate key parameters of the MSPE algorithm to further improve the accuracy of complexity diagnosis for hydrological series will be an important avenue of research.

Future changes in precipitation for identified sub‐regions in East Asia using bias‐corrected multi‐RCMs

AbstractWater management is a crucial issue in East Asia and is significantly influenced by climate change. Because East Asia's precipitation characteristics are varied and complex, it is necessary to implement the analysis of the future changes in precipitation on an objectively classified, specific sub‐regional level to effectively solve these problems. In this study, sub‐regions were objectively classified according to their precipitation characteristics, based on the present observations in East Asia. Additionally, the bias‐correction technique was applied to the regional climate model (RCM) to yield more reliable future precipitation predictions for the sub‐regions. We obtained the following four key results in this study. (1) The classified sub‐regions corresponded well to the observed annual precipitation distribution and adequately reflected the influence of the summer monsoon. The annual precipitation of the classified sub‐regions exhibited a decreasing trend in arid regions and an increasing trend in humid areas. (2) Because the bias‐corrected RCM was able to simulate annual precipitation reasonably, this technique was applied to future scenarios to analyse future changes in precipitation for each classified sub‐region. (3) Future annual precipitation will increase significantly in the region spanning from the western part of Southern China to the area around the Bohai Sea, while the temporal variations of the climatological daily precipitation indicated that sub‐regions under the influence of the East Asian summer monsoon (EASM) will be predicted an increase in second peaks during the summer. Hence, in the future, the EASM rain belt will be strengthened and its average location will move northward from its present location. (4) Because the high‐intensity extreme precipitation indices are expected to have a more considerable increase in all the classified sub‐regions, there is a need to solidify response measures against disasters that could arise from these changes in the East Asian region.

Spatio-temporal analysis of dry and wet periods in Iran by using Global Precipitation Climatology Center-Drought Index (GPCC-DI)

In this study, the temporal and spatial characteristics of droughts in Iran, including severity, duration, frequency, and extent, were studied using the GPCC-DI, in a 68-year period from 1952 to 2019. To display and analyze these features, time trend components, intensity, duration, and area diagrams, Mann-Kendall non-parametric test and Kriging geostatistical method were used. The result of plotting intensity-duration-frequency maps shows an increasing trend in regional drought severity and spread. It was also found that the most severe droughts occurred in the 12- and 24-month periods from 2000 onward. Mann-Kendall test results also show that significant increases in droughts occurred in the central desert basins of Hamoon basin and south Baluchistan in the southeastern Iran. In addition, decrease in occurrences of dry and wet periods happened in the western, northwestern, and coastal regions of the Caspian Sea. The pattern of spatial variations of drought severity indicated two major drought foci in southeast and central Iran. The number of drought centers in Iran has also increased over time. The results of the analysis of the time trends of dry and wet periods point to the occurrence of continuous droughts in 12- and 24-month periods.

Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon

AbstractIt is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980–2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high‐latitude region where much of the precipitation falls as snow. Leave‐one‐out cross‐validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long‐term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska.

Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part II: Daily Precipitation

AbstractStatistical downscaling (SD) derives localized information from larger-scale numerical models. Convolutional neural networks (CNNs) have learning and generalization abilities that can enhance the downscaling of gridded data (Part I of this study experimented with 2-m temperature). In this research, we adapt a semantic-segmentation CNN, called UNet, to the downscaling of daily precipitation in western North America, from the low resolution (LR) of 0.25° to the high resolution (HR) of 4-km grid spacings. We select LR precipitation, HR precipitation climatology, and elevation as inputs; train UNet over the subset of the south- and central-western United States using Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data from 2015 to 2018, and test it independently in all available domains from 2018 to 2019. We proposed an improved version of UNet, which we call Nest-UNet, by adding deep-layer aggregation and nested skip connections. Both the original UNet and Nest-UNet show generalization ability across different regions and outperform the SD baseline (bias-correction spatial disaggregation), with lower downscaling error and more accurate fine-grained textures. Nest-UNet also shares the highest amount of information with station observations and PRISM, indicating good ability to reduce the uncertainty of HR downscaling targets.

Evaluating the Performance of Secondary Precipitation Products through Statistical and Hydrological Modeling in a Mountainous Tropical Basin of India

This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.

Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison

This study analyzes the performance of precipitation estimates from historical runs of the CMIP6 (Climate Model Intercomparison Project Phase 6) over the climatic regions of Iran. In order to capture the spatio-temporal precipitation patterns, using a set of evaluation metrics, 12 GCMs (General Circulation Models) are compared to the observation data from the GPCC (Global Precipitation Climatology Centre) at the common 1° spatial resolution for 1950–2014. A comprehensive assessment is performed at different temporal scales including monthly, seasonal and annual. Results indicate that the reliability of precipitation estimates varies significantly across space and time. The CMIP6 models best reproduce the climatological features of precipitation and its spatio-temporal changes over the arid and hyper arid areas of the country. The outputs of the models exhibit less systematic biases in the arid zone. In addition, a strong underestimation is detected throughout the rainiest zone, indicating high uncertainty in wet regions. All models tend to show some level of underestimation in summer months with the lowest rainfall. The findings illustrate substantial inter-model variability over different climatic zones. Each of the CMIP6 models appears to be more suitable in a specific climatic zone. The models that performed reasonably well in the humid zone (CNRM-CM6-1 and MRI-ESM2-0), did not perform well in the hyper arid and arid zones. Similarly, models (HadGEM3-GC31-LL, BCC-CSM2-MR, and CanESM5) that performed well in the arid and hyper arid zones did not perform as well in the humid zone. Results inform what types of models are suitable for different climate zones.

Spatio-Temporal Assessment of Global Precipitation Products over the Largest Agriculture Region in Pakistan

Spatial and temporal precipitation data acquisition is highly important for hydro-meteorological applications. Gridded precipitation products (GPPs) offer an opportunity to estimate precipitation at different time and resolution. Though, the products have numerous discrepancies that need to be evaluated against in-situ records. The present study is the first of its kind to highlight the performance evaluation of gauge based (GB) and satellite based (SB) GPPs at annual, winter, and summer monsoon scale by using multiple statistical approach during the period of 1979–2017 and 2003–2017, respectively. The result revealed that the temporal magnitude of all the GPPs was different and deviate up to 100–200 mm with overall spatial pattern of underestimation (GB product) and overestimation (SB product) from north to south gradient. The degree of accuracy of GB products with observed precipitation decreases with the increase in the magnitude of precipitation and vice versa for SB precipitation products. Furthermore, the observed precipitation revealed the positive trend with multiple turning points during the period 1979–2005. However, the gentle increase with no obvious break point has been detected during the period of 2005–2017. The large inter-annual variability and trends slope of the reference data series were well captured by Global Precipitation Climatology Centre (GPCC) and Tropical Rainfall Measuring Mission (TRMM) products and outperformed the relative GPPs in terms of higher R2 values of ≥ 0.90 and lower values of estimated RME ≤ 25% at annual and summer monsoon season. However, Climate Research Unit (CRU) performed better during winter estimates as compared with in-situ records. In view of significant error and discrepancies, regional correction factors for each GPPs were introduced that can be useful for future concerned projects over the study region. The study highlights the importance of evaluation by the careful selection of potential GPPs for the future hydro-climate studies over the similar regions like Punjab Province.

Tracking Moisture Sources of Precipitation over Central Asia: A Study Based on the Water-Source-Tagging Method

AbstractCentral Asia is a semiarid to arid region that is sensitive to hydrological changes. We use the Community Atmosphere Model, version 5 (CAM5), equipped with a water-tagging capability, to investigate the major moisture sources for climatological precipitation and its long-term trends over central Asia. Europe, the North Atlantic Ocean, and local evaporation, which explain 33.2% ± 1.5%, 23.0% ± 2.5%, and 19.4% ± 2.2% of the precipitation, respectively, are identified as the most dominant moisture sources for northern central Asia (NCA). For precipitation over southern central Asia (SCA), Europe, the North Atlantic, and local evaporation contribute 25.4% ± 2.7%, 18.0% ± 1.7%, and 14.7% ± 1.9%, respectively. In addition, the contributions of South Asia (8.6% ± 1.7%) and the Indian Ocean (9.5% ± 2.0%) are also substantial for SCA. Modulated by the seasonal meridional shift in the subtropical westerly jet, moisture originating from the low and midlatitudes is important in winter, spring, and autumn, whereas northern Europe contributes more to summer precipitation. We also explain the observed drying trends over southeastern central Asia in spring and over NCA in summer during 1956–2005. The drying trend over southeastern central Asia in spring is mainly due to the decrease in local evaporation and weakened moisture fluxes from the Arabian Peninsula and Arabian Sea associated with the warming of the western Pacific Ocean. The drying trend over NCA in summer can be attributed to a decrease in local evaporation and reduced moisture from northern Europe that is due to the southward shift of the subtropical westerly jet.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

Precipitation Characteristics and Moisture Source Regions on Mt. Everest in the Khumbu, Nepal

Summary Precipitation is critical to the water towers of the Hindu Kush-Himalaya-Karakoram region, exerting an important control on glacier mass balance and the water resources for 1.65 billion people. Given that hydroclimatic extremes and water stress have emerged as key hazards in the context of climate change, Nepal's Khumbu region overlaps key vulnerabilities. Here, we investigate the region's precipitation characteristics and moisture sources through analysis of data from a new high-altitude network of automatic weather stations, which allows for a more complete understanding of the climatological precipitation data that are critical information for local communities in the Khumbu region, visitors, and downstream populations. Our findings demonstrate that the northern Bay of Bengal is potentially an important moisture source during the monsoon period (June to August) and that westerly trajectories over land predominate for precipitation events during the post-monsoon, winter, and pre-monsoon seasons.

Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria

This study compares multi model ensemble (MME) projections of rainfall using general quantile mapping, gamma quantile mapping, Power Transformation and Linear Scaling bias correction (BC) methods for representative concentration pathways (RCPs) 4.5 and 8.5 of the Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). Using the Global Precipitation Climatology Centre historical period (1961–2005) rainfall data as the reference, projection was conducted over 323 grid points of Nigeria for the periods 2010–2039, 2040–2069 and 2070–2099. The performances of the different BC methods in removing biases from the GCMs were assessed using different statistical indices. The computation of the MME of the projected rainfall was conducted by aggregation of 20 GCMs using random forest regression method. The percentage differences in the future rainfall relative to the historical period were estimated for all BC methods. Spatial projection of the percentage changes in rainfall for Linear scaling, which was the best performing BC method, showed increases in rainfall of 5.5–6.9% under RCPs 4.5 and 8.5, respectively, while the decrease range was −3.2–−4.2% respectively during the wet season. The range of annual increases in precipitation was 5.7–7.3% for RCP 4.5 and 8.5, respectively, while the decrease range was −1.0–−4.3%. This study also revealed monthly rainfall within the country will decrease during the wet season between June and September, which is a significant period where most crops need the water for growth. Findings from this study can be of importance to policy makers in the management of changes in hydrological processes due to climate change and management of related disasters such as floods and droughts.