Climate Prediction Center Research Articles

Evaluation of Multi-Satellite Precipitation Products and Their Ability in Capturing the Characteristics of Extreme Climate Events over the Yangtze River Basin, China

Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still difficult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study, the accuracies of seven SPPs on multiple spatiotemporal scales in the Yangtze River Basin (YRB) during the period of 2003–2017 are evaluated, along with their ability to capture ECE characteristics. The seven products are the Tropical Rainfall Measuring Mission, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (25), CHIRPS (05), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record, PERSIANN-Cloud Classification System, and Global Precipitation Measurement (GPM) IMERG. Rain gauge precipitation data provided by the China Meteorological Administration are adopted as reference data. Various statistical evaluation metrics and different ECE indexes are used to evaluate and compare the performances of the selected products. The results show that CMORPH has the best agreement with the reference data on the daily and annual scales, but GPM IMERG performs relatively well on the monthly scale. With regard to ECE monitoring in the YRB, in general, GPM IMERG and CMORPH provide higher precision. As regards the spatial heterogeneity of the SPP performance in the YRB, most of the examined SPPs have poor accuracy in the mountainous areas of the upper reach. Only CMORPH and GPM IMERG exhibit superior performance; this is because they feature an improved inversion precipitation algorithm for mountainous areas. Furthermore, most SPPs have poor ability to capture extreme precipitation in the estuaries of the lower reach and to monitor drought in the mountainous areas of the upper reach. This study can provide a reference for SPP selection for ECE analysis.

Investigating ENSO and its teleconnections under climate change in an ensemble view – a new perspective

Abstract. The changes in the El Niño–Southern Oscillation (ENSO) phenomenon and its precipitation-related teleconnections over the globe under climate change are investigated in the Community Earth System Model Large Ensemble from 1950 to 2100. For the investigation, a recently developed ensemble-based method, the snapshot empirical orthogonal function (SEOF) analysis, is used. The instantaneous ENSO pattern is defined as the leading mode of the SEOF analysis carried out at a given time instant over the ensemble. The corresponding principal components (PC1s) characterize the ENSO phases. By considering sea surface temperature (SST) regression maps, we find that the largest changes in the typical amplitude of SST fluctuations occur in the June–July–August–September (JJAS) season, in the Niño3–Niño3.4 (5∘ N–5∘ S, 170–90∘ W; NOAA Climate Prediction Center) region, and the western part of the Pacific Ocean; however, the increase is also considerable along the Equator in December–January–February (DJF). The Niño3 amplitude also shows an increase of about 20 % and 10 % in JJAS and DJF, respectively. The strength of the precipitation-related teleconnections of the ENSO is found to be nonstationary, as well. For example, the anticorrelation with precipitation in Australia in JJAS and the positive correlation in central and northern Africa in DJF are predicted to be more pronounced by the end of the 21th century. Half-year-lagged correlations, aiming to predict precipitation conditions from ENSO phases, are also studied. The Australian and Indonesian precipitation and that of the eastern part of Africa in both JJAS and DJF seem to be well predictable based on the ENSO phase, while the southern Indian precipitation relates to the half-year previous ENSO phase only in DJF. The strength of these connections increases, especially from the African region to the Arabian Peninsula.

Hydrological evaluation of hourly merged satellite–station precipitation product in the mountainous basin of China using a distributed hydrological model

AbstractOver the past two decades, various satellite‐based precipitation products have been released to provide uninterrupted time‐series precipitation with quasi‐global coverage. The latest Chinese merged satellite–gauge (CMS) rainfall product developed by the China Meteorological Administration (CMA) may reduce the errors in near real time by combining the Climate Prediction Centre's (CPC) morphing algorithm (CMORPH) rainfall estimates with over 30,000 weather station measurements, which therefore offer a potential alternative or supplement to ground‐based rainfall estimates for hydrometeorological applications in sparsely gauged or ungauged basins. In the present study, the characteristics of the CMS precipitation product were comprehensively evaluated for the period 2008–2012 over Danjiangkou basin, central China, and used to drive the Liuxihe (LXH)‐distributed hydrological model for detailed streamflow simulations. The results show that most of the small–median rainfall with an intensity < 25 mm·day–1 can be well captured by the CMS, and the simulated streamflow had relatively better agreement with observations for peak flow in the lower part of the basin. However, the total water amount was 15.7% less than the streamflow driven by rain gauges, on average. In addition, the depths of surface flow and groundwater forced by the CMS rainfall were 22.05% and 21.10% less than rain gauge, respectively. Overall, the study provides new insight into and experience of the hydrological applicability of the high spatiotemporal‐resolution satellite–gauge rainfall product for driving a distributed hydrological model at a daily resolution. This product has the potential to be an alternative data source for data‐sparse or ungauged basins for water resource management and hydrological simulation/prediction purposes.

Improving daily precipitation estimates for the Qinghai‐Tibetan plateau based on environmental similarity

AbstractDue to the scarcity of gauge observations and inaccuracy of satellite estimation, obtaining reliable daily precipitation estimates over the Qinghai‐Tibetan Plateau (QTP) remains challenging. In this article, an integrated scheme is developed based on the assumption that in a specific climatic region, the similarity of environmental conditions related to precipitation (SEP) in two locations is positively correlated to the similarities of occurrence and magnitude of precipitation between them. First, the QTP was divided into the northwestern arid, middle semi‐arid/semi‐humid, and southeastern humid climatic sub‐regions by grouping analysis. Second, based on modified weighted k‐nearest neighbour model, daily precipitation of target locations in these climatic sub‐regions were predicted by weighted regression of a group of gauge observations that have the largest SEP with the target locations. SEP was calculated by the following auxiliary environmental factors: longitude, latitude, elevation, Normalized Difference Vegetation Index, relative humidity, and CMORPH (Climate Prediction Center's morphing technique) daily precipitation estimates (original CMORPH). The validation results demonstrate the effectiveness of the proposed scheme. Compared with the original CMORPH and PDF‐calibrated CMORPH (CMORPH calibrated by probability density function matching plus optimal interpolation method) at daily, monthly, and yearly scales, the scheme improves the rain/no rain detection capacity and the accuracy of daily precipitation estimates. In addition, the daily precipitation estimates obtained from this scheme can present significant discrimination over specific geographic units, particularly the Qaidam Basin, the great bend of the Brahmaputra River, and Hengduan Mountain.

Evaluation of Satellite Precipitation Estimates over Australia

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?

Precipitation serves as a crucial factor in the study of hydrometeorology, ecology, and the atmosphere. Gridded precipitation data are available from a multitude of sources including precipitation retrieved by satellites, radar, the output of numerical weather prediction models, and extrapolation by ground rain gauge data. Evaluating different types of products in ungauged regions with complex terrain will not only help researchers in applying scientific data, but also provide useful information that can be used to improve gridded precipitation products. The present study aims to evaluate comprehensively 12 precipitation datasets made by raw retrieved products, blended with rain gauge data, and blended multiple source datasets in multi-temporal scales in order to develop a suitable method for creating gridded precipitation data in regions with snow-dominated regions with complex terrain. The results show that the Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation with Gauge Adjusted (GSMaP_GAUGE), Tropical Rainfall Measuring Mission (TRMM_3B42), Climate Prediction Center Morphing Technique blended with Chinese observations (CMORPH_SUN), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) can represent the spatial pattern of precipitation in arid/semi-arid and humid/semi-humid areas of the Qinghai-Tibet Plateau on a climatological spatial pattern. On interannual, seasonal, and monthly scales, the TRMM_3B42, GSMaP_GAUGE, CMORPH_SUN, and MSWEP outperformed the other products. In general, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN_CCS) has poor performance in basins of the Qinghai-Tibet Plateau. Most products overestimated the extreme indices of the 99th percentile of precipitation (R99), the maximal of daily precipitation in a year (Rmax), and the maximal of pentad accumulation of precipitation in a year (R5dmax). They were underestimated by the extreme index of the total number of days with daily precipitation less than 1 mm (dry day, DD). Compared to products blended with rain gauge data only, MSWEP blended with more data sources, and outperformed the other products. Therefore, multi-sources of blended precipitation should be the hotspot of regional and global precipitation research in the future.

Rainfall Event–Duration Thresholds for Landslide Occurrences in China

A rainfall threshold for landslide occurrence at a national scale in China has rarely been developed in the early warning system for landslides. Based on 771 landslide events that occurred in China during 1998–2017, four groups of rainfall thresholds at different quantile levels of the quantile regression for landslide occurrences in China are defined, which include the original rainfall event–duration (E–D) thresholds and normalized (the accumulated rainfall is normalized by mean annual precipitation) (EMAP–D) rainfall thresholds based on the merged rainfall and the Climate Prediction Center Morphing technique (CMORPH) rainfall products, respectively. Each group consists of four sub-thresholds in rainy season and non-rainy season, and both are divided into short duration (<48 h) and long duration (≥48 h). The results show that the slope of the regression line for the thresholds in the events with long durations is larger than that with short durations. In addition, the rainfall thresholds in the non-rainy season are generally lower than those in the rainy season. The E–D thresholds defined in this paper are generally lower than other thresholds in previous studies on a global scale, and a regional or national scale in China. This might be due to there being more landslide events used in this paper, as well as the combined effects of special geological environment, climate condition and human activities in China. Compared with the previous landslide model, the positive rates of the rainfall thresholds for landslides have increased by 16%–20%, 10%–17% and 20%–38% in the whole year, rainy season and non-rainy season, respectively.

Impact of PDO and AMO on interdecadal variability in extreme high temperatures in North China over the most recent 40-year period

Based on the 1979–2018 datasets of Climate Prediction Center (CPC) daily maximum air temperature, HadISST, and NCEP-DOE II reanalysis, the impact of Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO) on the interdecadal variability in extreme high temperature (EHT) in North China (NC) is investigated through observational analysis and National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 5.3 (CAM5.3) numerical simulations. The observational results show an interdecadal shift in NC’s EHT in approximately 1996 with a cold period from 1983 to 1996 and a warm period from 1997 to 2014. The summer PDO and AMO are both closely related to NC’s EHT, of which AMO dominates. From the cold to warm period, the combination of PDO and AMO changed from a positive PDO (+ PDO) phase and a negative AMO (− AMO) phase to a negative PDO (− PDO) phase and a positive AMO (+ AMO) phase. The shift in the antiphase combination of PDO and AMO plays an important role in the interdecadal transition of NC’s EHT in 1996. PDO could impact NC’s EHT through the Pacific-East Asia teleconnection pattern, and AMO could influence the NC’s EHT through an atmospheric wave train in the midlatitudes of the Northern Hemisphere. During the warm period (− PDO and + AMO), warmer sea surface temperature anomalies (SSTA) in the northern North Pacific (NP) and North Atlantic (NA) could cause anticyclonic circulation anomalies over these two basins. The anticyclonic circulations anomalies over the NP could enhance the anticyclone over NC through the Pacific-East Asian (PEA) teleconnection pattern. It could also cause an easterly wind from the NP to NC which would weaken the upper westerly over NC. The anticyclonic anomalies over the NA, which were parts of the wave train, could affect other sectors of the wave train, resulting in anticyclonic anomalies over NC. The anticyclonic anomalies over NC could strengthen the continental high and weaken the upper zonal westerly, resulting in favorable EHT conditions. During the cold period (+ PDO and − AMO), because of the same atmospheric response mechanism, a westerly wind from NC to NP and a wave train with reversed anomaly centers could be found, causing a cyclonic anomaly over NC that is not conducive to the EHT. A series of numerical simulations using CAM5.3 confirm the above observational results and show that the combination of + PDO and − AMO changing to − PDO and + AMO has a great impact on the interdecadal shift in EHT in NC in 1996. The simulations also show that both + AMO and − PDO can lead the EHT in NC individually, and the impact of AMO on the EHT in NC is dominant.

Evaluation of Daily Precipitation Product in China from the CMA Global Atmospheric Interim Reanalysis

The China Meteorological Administration (CMA) recently produced a CMA Global Atmospheric Interim Reanalysis (CRAI) dataset for the years 2007–2016. A comprehensive evaluation of the ability of CRAI to capture the spatiotemporal variability of observed precipitation, in terms of both mean states and extreme indicators over China, is performed. Comparisons are made with other current reanalysis datasets, namely, the ECMWF interim reanalysis (ERAI), Japanese 55-yr reanalysis (JRA55), NCEP Climate Forecast System Reanalysis (CFSR), and NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), as well as NCEP Climate Prediction Center (CPC) observations. The results show that, for daily variations of rainfall during warm seasons in eastern China, CRAI and CFSR overestimate the precipitation of the main rain belt, while the overestimation is confined to the area south of 25°N in JRA55 but north of 24°N in MERRA2; whereas ERAI tends to underestimate the precipitation in most regions of eastern China. Two extreme metrics, the total amount of precipitation on days where daily precipitation exceeds the 95th percentile (R95pTOT) and the number of consecutive dry days (CDDs) in one month, are examined to assess the performance of reanalysis datasets. In terms of extreme events, CRAI, ERAI, and JRA55 tend to underestimate the R95pTOT in most of eastern China, whereas more frequent extreme rainfall can be found in most regions of China in both CFSR and MERRA2; and all of the reanalyses underestimate the CDDs. Among the reanalysis products, CRAI and JRA55 show better agreement with the observed R95pTOT than the other datasets, with fewer biases, higher correlation coefficients, and much more similar linear trend patterns, while ERAI stands out in better capturing the amount and temporal variations of the observed CDDs.

Dataset of high temperature extremes over the major land areas of the Belt and Road for 1979-2018

ABSTRACT Hot temperature extremes exert strong negative socioeconomic and environmental impacts worldwide. In this paper, we focus on the major land areas of the Belt and Road (BR) and develop a dataset of high temperature extremes including hot days, hot nights and combined hot extremes using Climate Prediction Center (CPC) gridded daily temperature data at a resolution of 0.5° during 1979–2018. The constant thresholds of daily maximum surface air temperature (Tmax) include 28°C, 30°C, 32°C, 35°C, 38°C, and 40°C, while those of daily minimum surface air temperature (Tmin) consist of 20°C, 25°C, and 30°C. Data of hot days, hot nights and combined hot extreme are produced based on different constant Tmax and Tmin thresholds and their combination. We also adopt the 90th and 95th percentile thresholds of Tmax and Tmin to generate high temperature extreme data. The validation with China Meteorological Administration (CMA) data over eastern China for the period of 1979–2011 indicates that our dataset can generally describe the spatial and temporal variations of high temperature extremes well. Potential applications of this dataset include analyses on the local-to-regional characteristics of high temperature extremes and their impacts on various sectors over the major BR land areas. The dataset is available at http://www.sciencedb.cn/dataSet/handle/904.

Quasi-Global Evaluation of IMERG and GSMaP Precipitation Products over Land Using Gauge Observations

Understanding the error distribution of satellite precipitation products is conducive to obtaining accurate precipitation data, which is a very important input parameter in hydrological models and climate models. The error characteristics of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) uncalibrated products on quasi-global land and six continents are evaluated, and the effects of latitude, elevation, and season on satellite precipitation product accuracy are analyzed. In order to be consistent with the Climate Prediction Center (CPC), the selected products are resampled at 0.5° and daily resolutions from 1 January 2015 to 31 August 2018. We find out that (1) GSMaP performs worse than IMERG mainly due to systematic errors and poor performance at high latitudes; (2) overestimation is obvious in high latitude areas of the northern hemisphere and also in areas with low rainfall intensity; (3) IMERG and GSMaP show good performance in summer and poor performance in winter; (4) where elevation is lower than 1500 m, the error metrics are highly correlated with the elevation; (5) the correlation coefficient is relatively high in areas with high rainfall, and the dispersion of satellite data and gauge data is also high. IMERG is a high-quality satellite precipitation product in the GPM era, but some uncertainties mentioned above are still worthy of attention by product developers and users.

Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India

The study analysed the spatiotemporal variation in extreme precipitation and temperature at the daily scale across India using eight indices of climate change suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this analysis, latest high-resolution India Meteorological Department (IMD) data for the period 1971–2017 (precipitation) and 1971–2013 (temperature) are used along with global gridded reanalysis products. The trends are evaluated using non-parametric Mann-Kendall (MK) test and regression analysis. At the annual scale, about 13% of the locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices for the future (using CanESM2 projected data for RCP8.5 after suitable bias correction) show significant increasing (decreasing) trend in warm days (cold days) in most (49% to 84%) of the locations. Further, most locations (varying from 60 to 81%) show an increasing trend in warm nights and a decreasing trend in cold nights. Similar analysis for the historical and future period are also performed using Climate Prediction Centre (CPC) reanalysis data as the reference and the trends, on comparison with IMD data, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.

Suitability of global precipitation estimates for hydrologic prediction in the main watersheds of Upper Awash basin

Unavailability of optimum network of rain gauges and lack of well-organized precipitation data are one of the main challenges in hydrologic and climate-related studies. This study aims to evaluate the performance of three global precipitation data obtained from the Climate Forecast System Reanalysis (CFSR), Climate Prediction Center (CPC) and Tropical Rainfall Measuring Mission (TRMM) for hydrologic prediction at four main watersheds in the Upper Awash basin. Statistical indices and graphical efficiency measures were used to examine the CFSR-NCEP, CPC-NOAA, and TRMM data with the gauged data on mean annual, monthly, and daily basis at Upper Basin (UB) and Lower Basin (LB) area. The performance of global precipitation estimates was encouraging. Moreover, the spatial map of annual precipitation products demonstrated comparable spatial patterns with observed annual precipitation. The Soil and Water Assessment Tool (SWAT) model was used for hydrological simulation, and the hydrologic simulation efficiency of gauged data and global precipitation revealed good performance with R2 and NS value of > 0.5 for daily, and > 0.7 for monthly scale simulations. However, lower to satisfactory model performance was obtained at the daily simulation in the Mojo and Akaki watersheds. The CPC-NOAA precipitation data was found superior in capturing the hydrologic variability. In relatively smaller watersheds, the suitability of global precipitation was lower due to the limited number of data points resulted from the coarse resolution of the data sources. Improvement in the satellite meteorology and climate reanalysis data could substantially increase the availability of alternative climate data for hydrologic studies.

An Assessment of the Seasonal Rainfall and Its Societal Implications in Zanzibar Islands during the Season of October to December, 2019

The assessment of the performance of the October to December (OND), 2019 rainfall season in Zanzibar (Unguja and Pemba) with reference to local downscaled Tanzania Meteorological Authority (TMA) forecast, and regional (Intergovernmental Authority on Development Climate Prediction and Application Center (IGAD-ICPAC) weather forecasts were assessed by comparing the long term average of OND rainfall data and previous OND rainfall seasons of 2016, 2017 and 2018 as well as extreme positive Indian Ocean Dipole (IOD) during OND seasons of 1961, 1994, 1997, 2006 and 2019 for Zanzibar. The study assessed zonal (u) and meridional (v) winds at 850 and 200 mb, monthly and dekadal sea surface temperature (SST); the Madden Julien Oscillations (MJO) forecast reports and the ocean heat content data. Both gridded and observed datasets were processed into dekadal, monthly and seasonal averages and then analysed. The results revealed that, based on the observations, above normal rainfall of 936 and 908 mm were reported at stations of Kisauni (Unguja) and Karume airport (Pemba) during 2019 OND season. This amount was the first and second ever recorded for the extreme positive IOD during OND seasons of 1961, 1994, 1997, 2006 and 2019, and also the first for the previous higher OND rainfall seasons of 2016, 2017 and 2018 which was highly variable. Moreover, these values were second ever-recorded highest OND rainfall since 1916 to 2019 where the first one was observed in 1961. Furthermore, the results revealed that 2019 OND seasonal rainfall had the highest amount of contribution based on historical climatology. For instance, the 2019 OND rainfall for Kisauni, Pemba airport and Matangatuani contributed to 198%, 303% and 231% of the long term (1987-2016) mean OND rainfall in Zanzibar. Indeed, the results show that the presence of the MJO during OND and the enhanced positive IOD was among the causes for the observed wetness of the 2019 OND in Zanzibar and most parts of the country. Moreover, the dominant easterly, southeasterly and northwesterly onshore winds during 2019 OND also contributed to heavy rainfall. The monthly rainfall variability among stations had the highest amount of rainfall which ranges from 400 to 500 mm which was observed during October in Kisauni and Karume airport, while the lowest amount ranging from 150 to 180 mm was observed during November in Matangatuani and the surrounding stations. Based on the comparison of the forecasted reports issued by ICPAC and TMA, the results revealed that irrespective of not considering the likelihood of occurrence of MJO and strong positive IOD both forecasts has performed well with that of ICPAC being leading. Conclusively, since the 2019 OND season has been uniquely characterized by the presence of MJO and IOD polarities it would be worthful to consider the two as input parameters during the OND rainfall forecast over the region.

Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan

AbstractMerged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide higher potentials to hydrological applications. This study applied a dynamic clustered Bayesian model averaging (DCBA) algorithm to merge four SPPs across Pakistan. The DCBA algorithm produced dynamic weights to different SPPs varying both spatially and temporally to accommodate the spatiotemporal differences of SPP performances. The MMPD is developed at daily temporal scale from 2000 to 2015 with spatial resolution of 0.25° using extensively evaluated SPPs and a global atmospheric reanalysis–precipitation dataset: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing technique (CMORPH), and ERA-Interim. The DCBA algorithm is evaluated across four distinct climate regions of Pakistan over 102 ground precipitation gauges (GPGs). DCBA forecasting outperformed all four SPPs with average Theil’s U of 0.49, 0.38, 0.37, and 0.36 in glacial, humid, arid, and hyperarid regions, respectively. The average mean bias error (MBE), mean error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and standard deviation (SD) of DCBA over all of Pakistan are 0.54, 1.40, 4.94, 0.77, and 5.17 mm day−1, respectively. Seasonal evaluation revealed a dependency of DCBA performance on precipitation magnitude/intensity and elevation. Relatively poor DCBA performance is observed in premonsoon/monsoon seasons and at high/mild elevated regions. Average improvements of DCBA in comparison with TMPA are 59.56% (MBE), 49.37% (MAE), 45.89% (RMSE), 19.48% (CC), 46.7% (SD), and 18.66% (Theil’s U). Furthermore, DCBA efficiently captured extreme precipitation trends (premonsoon/monsoon seasons).

An Assessment of Spatial Distribution of Four Different Satellite-Derived Rainfall Estimations and Observed Precipitation over Bangladesh

Given that precipitation is a major component of the earth’s water and energy cycles, reliable information on the monthly spatial distribution of precipitation is also crucial for climate science, climatological water-resource research studies, and for the evaluation of regional model simulations. In this paper, four satellite derived precipitation datasets: Climate Prediction Center MORPHING (CMORPH), Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation Algorithm from Remotely-Sensed Information using an Artificial Neural Network (PERSIANN), and the global Satellite Mapping of Precipitation (GSMaP) are spatially analyzed and compared with the observed precipitation data provided by Bangladesh Meteorological Department (BMD). For this study, the different precipitations data sets are spatially analyzed from 2nd May 2019 to 4th May 2019 at the time of Cyclone “FANI”. It is found that the satellite derived precipitation datasets are reasonably matched with the observed but slightly different.

Using Visualization Science to Improve Expert and Public Understanding of Probabilistic Temperature and Precipitation Outlooks

AbstractVisually communicating temperature and precipitation climate outlook graphics is challenging because it requires the viewer to be familiar with probabilities as well as to have the visual literacy to interpret geospatial forecast uncertainty. In addition, the visualization scientific literature has open questions on which visual design choices are the most effective at expressing the multidimensionality of uncertain forecasts, leaving designers with a lack of concrete guidance. Using a two-phase experimental setup, this study shows how recently developed visualization diagnostic guidelines can be used to iteratively diagnose, redesign, and test the understandability the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) climate outlooks. In the first phase, visualization diagnostic guidelines were used in conjunction with interviews and focus groups to identify understandability challenges of existing visual conventions in temperature and precipitation outlooks. Next, in a randomized control versus experimental treatment setup, several graphic modifications were produced and tested via an online survey of end users and the general public. Results show that, overall, end users exhibit a better understanding of outlooks, but some types of probabilistic color mapping are misunderstood by both end users and the general public, which was predicted by the diagnostic guidelines. Modifications lead to significant gains in end-user and general public understanding of climate outlooks, providing additional evidence for the utility of using control versus treatment testing informed by visualization diagnostics.

Evaluation of multiple gridded precipitation datasets for the arid region of northwestern China

Abstract Gridded precipitation datasets for the arid region of northwestern China (ARNC) are thought to have large discrepancies. This study systematically evaluated the performance of multiple datasets for the ARNC over the period 1980–2014 using the observed gridded precipitation dataset (OBS) from the China Meteorological Administration (CMA). The various datasets include two National Center for Environmental Prediction (NCEP) reanalyses (NCEP-1 and NCEP-2), two Climate Prediction Center (CPC) Merged Analyses of Precipitation (CMAP-1 and CMAP-2), the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-Interim), the Global Precipitation Climatology Project (GPCP), the Climatic Research Unit, University of East Anglia, UK (CRU), and the second Modern-Era Retrospective Analysis for Research and Application system (MERRA-2). The results show that all the multiple datasets reasonably reproduce the climatology, seasonality, interannual variability, and spatiotemporal patterns of ARNC precipitation, but there are discrepancies in the long-term trends. MERRA-2 and CMAP-2 best capture the spatial distributions and inter-annual variability of mean annual precipitation (MAP). CRU best reproduced the seasonality and temporal variability of MAP. However, ERA-Interim and NCEP overestimated the precipitation amount in mountainous regions, and MERRA-2 underestimated precipitation in almost all seasons. Empirical orthogonal function (EOF) analysis indicated that GPCP and CMAP-2 better described both the spatial modes and principal components of the first two EOF patterns. The systematic evaluation in this study indicated that, overall, most of the satellite-merged datasets (GPCP, MERRA-2, and CMAP-2) perform better than gauge-based and reanalysis datasets alone. This study demonstrates that a systematic evaluation of the differences between multiple datasets is critical for reducing discrepancies.

An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

Merging satellite precipitation products tends to reduce the errors associated with individual satellite precipitation products and has higher potential for hydrological applications. The current study evaluates the performance of merged multi-satellite precipitation dataset (daily temporal and 0.25° spatial resolution) developed using the Dynamic Bayesian Model Averaging algorithm across four different climate regions, i.e., glacial, humid, arid and hyper-arid regions, of Pakistan during 2000–2015. Four extensively evaluated SPPs over Pakistan, i.e., Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Prediction Center MORPHing technique (CMORPH), and Era-Interim, are used to develop the merged multi-satellite precipitation dataset. Six statistical indices, including Mean Bias Error, Mean Absolute Error, Root Mean Square Error, Correlation Coefficient, Kling-Gupta efficiency, and Theil’s U coefficient, are used to evaluate the performance of merged multi-satellite precipitation dataset over 102 ground precipitation gauges both spatially and temporally. Moreover, the ensemble spread score and standard deviation are also used to depict the spread and variation of precipitation of merged multi-satellite precipitation dataset. Skill scores for all statistical indices are also included in the analyses, which shows improvement of merged multi-satellite precipitation dataset against Simple Model Averaging. The results revealed that DBMA-MSPD assigned higher weights to TMPA (0.32) and PERSIANN-CDR (0.27). TMPA presented higher skills in glacial and humid regions with average weights of 0.32 and 0.37 as compared to PERSIANN-CDR of 0.27 and 0.25, respectively. TMPA and Era-Interim depicted higher skills during pre-monsoon and monsoon seasons, with average weights of 0.31 and 0.52 (TMPA) and 0.25 and 0.21 (Era-Interim), respectively. Merged multi-satellite precipitation dataset overestimated precipitation in glacial/humid regions and showed poor performance, with the poorest values of mean absolute error (2.69 mm/day), root mean square error (11.96 mm/day), correlation coefficient (0.41), Kling-Gupta efficiency score (0.33) and Theil’s U (0.70) at some stations in glacial/humid regions. Higher performance is observed in hyper-arid region, with the best values of 0.71 mm/day, 1.72 mm/day, 0.84, 0.93, and 0.37 for mean absolute error, root mean square error, correlation coefficient, Kling-Gupta Efficiency score, and Theil’s U, respectively. Merged multi-Satellite Precipitation Dataset demonstrated significant improvements as compared to TMPA across all climate regions with average improvements of 45.26% (mean bias error), 30.99% (mean absolute error), 30.1% (root mean square error), 11.34% (correlation coefficient), 9.53% (Kling-Gupta efficiency score) and 8.86% (Theil’s U). The ensemble spread and variation of DBMA-MSPD calculated using ensemble spread score and standard deviation demonstrates high spread (11.38 mm/day) and variation (12.58 mm/day) during monsoon season in the humid and glacial regions, respectively. Moreover, the improvements of DBMA-MSPD quantified against fixed weight SMA-MSPD reveals supremacy of DBMA-MSPD, higher improvements (40–50%) in glacial and humid regions.

A Comparison of the Accuracy of Multi-satellite Precipitation Estimation and Ground Meteorological Records Over Southwestern Nigeria

Multi-satellite rainfall observations are compared with ground-based meteorological stations data in this study. Both ground and satellite meteorological datasets were analysed and synchronized in this study. Satellite rainfall datasets used are Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and African Rainfall Estimation (RFE 2.0), which were obtained from the archives of the NOAA Climate Prediction Centre. The ground meteorological data were obtained from the Nigerian Meteorological Agency (NIMET) Lagos office in Oshodi, for selected stations in Southwest of Nigeria, for the period 1998–2016. Descriptive and inferential analyses were used to analyse the dataset. Spatial and temporal trends analyses were carried out to compare rainfall estimates from satellite and ground stations. Generally, correlation results show that the relationship between satellite datasets and ground observation (NIMET) appears remarkably high in all stations with R2> 0.70. This relationship is greatly strong between TRMM and NIMET in Abeokuta, Akure, and Osogbo with R2 = 0.99, 0.98 and 0.98, respectively. CHIRPS also show high correlation with NIMET in these three stations with R2> 0.90, but RFE-NIMET relationship appears much more in Osogbo with R2 > 0.90. It is also obvious that TRMM and CHIRPS have a very high relationship in almost all locations with R2> 0.87. The major finding from this study is that though in situ meteorological data may be used for local studies of small areas, satellite rainfall estimates may also be used to augment such data, over a larger area. Besides, satellite rainfall estimates are more useful for areas with larger spatial and temporal coverage.