Climate Prediction Center Morphing Technique Research Articles

Enhancing Quantitative Precipitation Estimation of NWP Model With Fundamental Meteorological Variables and Transformer Based Deep Learning Model

AbstractQuantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India

In-situ precipitation data is the most reliable data for the drought assessment. However, gridded datasets are commonly used due to limited availability of station data. However, the gridded data may exhibit bias compared to station data. The present study has been undertaken to examine the performance of gridded datasets from global precipitation products (gauge-interpolated, merged from various sources, re-analysis and satellite products) for the assessment of meteorological drought with reference to station datasets of the India Meteorological Department (IMD) from 2000 to 2019 at six drought zones over India. Various statistical metrics were used to check the performance of the precipitation products at four seasons of India. The Standardised Precipitation Index (SPI) was used at a 1-month timescale to evaluate the meteorological drought characteristics. The gridded precipitation products, in regions of high rainfall, tended to underestimate precipitation during the monsoon season and overestimate it during the non-monsoon season. When precipitation was analysed spatially using statistical parameters, the product Integrated Multi-Satellite Retrievals (IMERG Final-Run) outperformed all the other precipitation products in the four seasons and six drought zones over India. For drought characteristics evaluation, the products Climate Prediction Center (CPC) Morphing Technique (CMORPH) and Global Precipitation Climatology Centre (GPCC) outperformed all the other products in the six drought zones over India. Further, in order to rank the precipitation products, the Spearman rank correlation coefficient (SCC) and critical success index (CSI) were used. The analysis results showed that IMERG Final-Run, GPCC and Multi-Source Weighted-Ensemble Precipitation (MSWEP-PastNoGauge) ranked best out of all the precipitation products when analysed for drought monitoring and assessment at six drought zones over India. Overall, IMERG Final-Run outperformed all the other precipitation products for drought monitoring, while TerraClimate performed poorly in most of the drought zones and in India as a whole. The study's findings will help choose suitable gridded precipitation dataset for meteorological drought assessment across six homogeneous drought zones and in India as a whole, aiding operational drought risk management and early warnings.

Assessment of Precipitation and Evapotranspiration in an Urban Area Using Remote Sensing Products (CHIRP, CMORPH, and SSEBop): The Case of the Metropolitan Region of Belem, Amazon

The aim of this study was to assess precipitation (P) and actual evapotranspiration (ET) by analyzing data from in situ stations compared with remote sensing products. Climate Hazards Center InfraRed Precipitation (CHIRP) and Climate Prediction Center morphing technique (CMORPH) were used for P and Operational Simplified Surface Energy Balance (SSEBop) was used for ET. The P in situ data for six stations were also compared to a reference station in the city. ET was analyzed for a single in situ station. The region chosen for this study was the Metropolitan Area of Belem (MAB), close to the estuary of the Amazon River and the mouth of the Tocantins River. Belem is the rainiest state capital in Brazil, which causes a myriad of challenges for the local population. The assessment was performed using the statistical metrics root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), mean bias error (MBE), coefficient of determination (R2), regression slope, and Nash–Sutcliffe coefficient (NS). For the reference station, the automatic and conventional CHIRP and CMORPH results, in mm/month, were as follows: automatic CHIRP: RMSE = 93.3, NRMSE = 0.32, MBE = −33.54, R2 = 0.7048, Slope = 0.945, NS = 0.5668; CMORPH: RMSE = 195.93, NRMSE = 0.37, MBE = −52.86, R2 = 0.6731, Slope = 0.93, NS = 0.4344; conventional station CHIRP: RMSE = 94.87, NRMSE = 0.32, MBE = −33.54, R2 = 0.7048, Slope = 0.945, NS = 0.5668; CMORPH: RMSE = 105.58, NRMSE = 0.38, MBE = −59.46 R2 = 0.7728, Slope = 1.007, NS = 0.4308. In the MAB region, ET ranges on average between 83 mm/month in the Amazonian summer and 112 mm/month in the Amazonian winter. This work concludes that, although CMORPH has a coarser resolution than CHIRP for the MAB at a monthly resolution, both remote sensing products were reliable. SSEBop also showed acceptable performance. For analyses of the consistency of precipitation time series, these products could provide more accurate information. The present study validates P and ET from remote sensing products with station data in the rain-dominated urban MAB.

Tropical Cyclone Rainfall Climatology, Extremes, and Flooding Potential from Remote Sensing and Reanalysis Datasets over the Continental United States

Abstract Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002–19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission–Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV. Significance Statement Tropical cyclones (TCs) can produce extreme rainfall and widespread flooding. Improvements in our preparedness, mitigation, and adaptation efforts rest on a better understanding of TC rainfall and its impact. Assessment of various rainfall products and uncertainties is urgently needed for decision making and other applications. Different quantitative precipitation estimate (QPE) datasets have been developed over the past decades. This study uses 18 years of data to better understand how key properties of TC rainfall, including its contribution to extreme events and flooding potential, are represented across five widely used QPE products. Our analysis shows that satellite-based, and to a lesser extent, reanalysis-based QPEs underestimate TC rainfall properties both in terms of climatology and extreme events compared to ground-radar-gauge-based QPE. The uncertainty increases in the coastal region and is reflected in the estimated TC flooding potential.

Assessment and Data Fusion of Satellite-Based Precipitation Estimation Products over Ungauged Areas Based on Triple Collocation without In Situ Observations

Reliable assessment of satellite-based precipitation estimation (SPE) and production of more accurate precipitation data by data fusion is typically challenging in sparsely gauged and ungauged areas. Triple collocation (TC) is a novel assessment approach that does not require gauge observations; it provides a feasible solution for this problem. This study comprehensively validates the TC performance for assessing SPEs and performs data fusion of multiple SPEs using the TC-based merging (TCM) approach. The study area is the Tibetan Plateau (TP), a typical area lacking gauge observations. Three widely used SPEs are used: the integrated multi-satellite retrievals for global precipitation measurement (IMERG) “early run” product (IMERG-E), the precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) dynamic infrared (PDIR), and the Climate Prediction Center (CPC) morphing technique (CMORPH). Validation of the TC assessment approach shows that TC can effectively assess the SPEs’ accuracy, derive the spatial accuracy pattern of the SPEs, and reveal the accuracy ranking of the SPEs. TC can also detect the SPEs’ accuracy patterns, which are difficult to obtain from a traditional approach. The data fusion results of the SPEs show that TCM incorporates the regional advantages of the individual SPEs, providing more accurate precipitation data than the original SPEs, revealing that data fusion is reasonable and reliable in ungauged areas. In general, the TC approach performs well for the assessment and data fusion of SPEs, showing reasonable applicability in the TP and other areas lacking gauge data than other methods because it does not rely on gauge observations.

Performance evaluation of multiple satellite rainfall data sets in central highlands of Abbay Basin, Ethiopia

ABSTRACT This study evaluates four satellite-based precipitation datasets with gauged rainfall observations at a daily and wet season time scales. Satellite Precipitation Estimators from Climate Hazards Group Infrared Precipitation Stations (CHIRPSv2), the Climate prediction center (CPC) morphing technique (CMORPH), the Integrated Multi-satellite Retrieval for GPM (IMERG-06) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSSIANN-CCS) were used. Categorical and continuous statistical techniques were applied to evaluate the detection and estimation ability of SPEs. Among the SPEs, CMORPH was superior, 0.90 and followed by IMERG-06, 0.82 in the Probability of detection (POD), Critical success index (CSI), 0.74 and 0.70 and Accuracy, 0.75 and 0.72 at wet season, respectively. The result of bias ratio showed underestimation for all SPEs at wet season and daily time scale. The comparison of SPEs in terms of RMSE indicated that IMERG-06 was better, 75.9 followed by CMORPH, 86.0. In terms of Relative bias (RBias), IMERG-06 was the second next to CHIRPSv2. On the other hand, IMERG-06 had better RMSE at JJAS and daily time scale. Overall, the findings of this evaluation study showed the capacity of IMERG-06 product could be reliable rainfall data sources for climate and hydrological analysis of central highlands of Abbay Basin.

An Enhanced Storm Warning and Nowcasting Model in Pre-Convection Environments

A storm tracking and nowcasting model was developed for the contiguous US (CONUS) by combining observations from the advanced baseline imager (ABI) and numerical weather prediction (NWP) short-range forecast data, along with the precipitation rate from CMORPH (the Climate Prediction Center morphing technique). A random forest based model was adopted by using the maximum precipitation rate as the benchmark for convection intensity, with the location and time of storms optimized by using optical flow (OF) and continuous tracking. Comparative evaluations showed that the optimized models had higher accuracy for severe storms with areas equal to or larger than 5000 km2 over smaller samples, and loweraccuracy for cases smaller than 1000 km2, while models with sample-balancing applied showed higher possibilities of detection (PODs). A typical convective event from August 2019 was presented to illustrate the application of the nowcasting model on local severe storm (LSS) identification and warnings in the pre-convection stage; the model successfully provided warnings with a lead time of 1–2 h before heavy rainfall. Importance score analysis showed that the overall impact from ABI observations was much higher than that from NWP, with the brightness temperature difference between 6.2 and 10.3 microns ranking at the top in terms of feature importance.

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

Abstract This study investigates the utility of satellite-based rainfall products and the performance of bias correction methods in one of the sub-basins of the Upper Blue Nile Basin (Main Beles basin). Four satellite rainfall products are used: Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 (TMPA 3B42V7), and Climate Forecast System Reanalysis (CFSR). The performance of the satellite rainfall products (SRPs) was compared using three bias correction methods (Delta, Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) on five metrological stations. Six statistical criteria were used to evaluate these methods on the period 2003–2016 at daily and monthly scales. The results showed that SRPs and bias correction methods of CMORPH_QM (r = 0.538) and TMPA_3B42V7_EQM (r = 0.95) data showed good performance, while PERSIANN_EQM (r = 0.348) and PERSIANN_Delta (r = 0.83) performed worst at daily and monthly time scales, respectively. This study assessed the importance of SRPs and bias correction methods to use in data scarce regions for water resources planning and other related sectors.

Mapping Australia’s precipitation: harnessing the synergies of multi-satellite remote sensing and gauge network data

ABSTRACT Coverage, resolution, and accuracy in the spatial and temporal estimates of remotely sensed precipitation from space satellites, along with the number of instruments deployed to deliver these observations, are increasing. Of key interest in this study is the unsurpassed opportunity offered by their broad and continuous coverage to complement sparse, but more accurate, in situ rain gauge measurements for building climate resilience at the local, regional, and global scales. For many parts of the globe, this opportunity remains untapped. Australia is no exception and provides a unique challenge, given the small fraction of the continent that has rain gauges, the highly diverse climate due to its large size, and the apparent worsening of extreme weather events in both frequency and intensity. Notwithstanding this great impetus, a continent-wide record of multi-satellite-gauge fused precipitation data for Australia remains lacking. This missing data asset is a prerequisite for understanding the emerging spatiotemporal dynamics of precipitation, without which reliable forecasts would be difficult if not impossible. This study seeks to address this need. Here, we develop a method which can synergistically fuse precipitation data from different sources. More specifically, the aim of this study is to develop Precipitation Profiler-Observation Fusion and Estimation (PPrOFusE), a tool to deliver high-quality gauge and multi-satellite fused precipitation data. We test and apply this tool for Australia, but it is by no means limited in scope to this region. By design, PPrOFusE has a built-in capability to assess the strengths and weaknesses of each platform. In this case study, we fused data for a period of 22 years (2000–2022) using the rain gauge network data from the Australian Bureau of Meteorology (BOM) together with satellite data from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Center Morphing technique (CMORPH). Our proposed precipitation data fusion method consists of two steps. Step 1, the relationship among the three sources of data is modeled by multiple linear regression at each rain gauge location, returning the least squares estimates for the associated regression coefficient vector. Step 2, such regression coefficient vector estimates for all rain gauge locations are fitted by a spatial autoregression model, whereafter the multiple linear regression coefficient vectors for those locations void of rain gauges are predicted by spatial interpolation. Key findings are twofold. First, CMORPH is more accurate for most regions of Australia than GSMaP. Second, a clustering analysis of the fused precipitation over the last 20 years suggests two key trends on Australia’s changing climate, relative to BOM’s six major climate zones, from the previous century: (a) increased spatial variability to the north, consistent with meteorological expectations, amid a southwards expansion of the wet summer dominant zones across the continent; (b) the edge of the arid region shifts southwards and pushes out Mediterranean climate and winter dominant rainfall zones across southern Australia.

Precipitation over the U.S. Coastal Land/Water Using Gauge-Corrected Multi-Radar/Multi-Sensor System and Three Satellite Products

The weather and climate over the coastal regions have received increasing attention because of substantial population growth, the rising sea level, and extreme weather. Satellite remote sensing provides global precipitation estimates (including coastal land/ocean). While these datasets have been extensively evaluated over land, they have rarely been assessed over coastal ocean. As precipitation radars cover both coastal land and ocean, we used the Multi-Radar/Multi-Sensor System (MRMS) gauge-corrected precipitation product from 2018 to 2020 to evaluate three widely used satellite-based precipitation products over the U.S. coastal land versus the ocean (and the water over the Great Lakes). These products included the Integrated Multi-satellite Retrievals for GPM (IMERG), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center Morphing technique (CMORPH). The MRMS data showed a precipitation climatology difference between the coastal land and the ocean that was higher in the winter and lower in the summer and autumn. IMERG and CMORPH performed best over land and water, respectively, while PERSIANN was the most consistent in its performance over land versus water. Heavy precipitation was overestimated by the three products, with larger overestimates over water than over land. These results were not affected by the MRMS uncertainties due to the gauge correction or by the use of different versions.

Comprehensive Evaluation and Error-Component Analysis of Four Satellite-Based Precipitation Estimates against Gauged Rainfall over Mainland China

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and the source of the errors within IMERG over diverse climate regions still need to be quantified. To this end, the final run gauge-calibrated IMERG V06 (V06C) and uncalibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices and two error decomposition schemes are used to quantify their performance. Key results are as follows. (1) Except for V06UC’s relatively high underestimation over the Tibetan Plateau (TP) and high overestimation over Xinjiang (XJ), Northeastern China (DB), and Northern China (HB) and CDR’s severe overestimation over TP, all four satellite-based precipitation products can generally capture the spatial pattern of precipitation over China. Moreover, the satellite-based precipitation estimates agree better with gauge observations over humid regions than over semi-humid, semi-arid, and arid regions. (2) All the statistical indicators show that CDR has the worst performance, whereas BLD is the best precipitation product. As for the two IMERG products, V06C has improved V06UC’s precipitation estimate. Results show that the gauge calibration algorithm (GCA) used in IMERG has active effect in terms of r, POD, and CSI. (3) Within all subregions, all four satellite-based precipitation products demonstrate their worst performance over the arid XJ region which exhibits the highest FAR and lowest POD and CSI values among all regions. (4) In terms of intensity distribution, for summer over China, the four satellite-based precipitation products generally overestimate the frequency of moderate precipitation and light precipitation events (<25 mm/day) and underestimate heavy precipitation events (>42 mm/day). (5) The relative bias ratio (RBR) analysis shows that the contribution of missed precipitation tends to be lower over wetter regions. In addition, for the same climate region, the contribution of missed precipitation is clearly lower in summer than in winter. In summer, false precipitation dominates the total error, whereas missed and false precipitation are the two leading error sources in winter. Future algorithm refinement efforts should focus on decreasing FAR in summer and winter and improving missed snow events during the winter.

Hydrological Modeling in the Chaohu Lake Basin of China—Driven by Open-Access Gridded Meteorological and Remote Sensing Precipitation Products

This study assessed the performance of two well-known gridded meteorological datasets, CFSR (Climate Forecast System Reanalysis) and CMADS (China Meteorological Assimilation Driving Datasets), and three satellite-based precipitation datasets, TRMM (Tropical Rainfall Measuring Mission), CMORPH (Climate Prediction Center morphing technique), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), in driving the SWAT (Soil and Water Assessment Tool) model for streamflow simulation in the Fengle watershed in the middle–lower Yangtze Plain, China. Eighteen model scenarios were generated by forcing the SWAT model with different combinations of three meteorological datasets and six precipitation datasets. Our results showed that (1) the three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) generally provided more accurate precipitation estimates than CFSR and CMADS. CFSR and CMADS agreed fairly well with the gauged measurements in maximum temperature, minimum temperature, and relative humidity, but large discrepancies existed for the solar radiation and wind speed. (2) The impact of precipitation data on simulated streamflow was much larger than that of other meteorological variables. Satisfactory simulations were achieved using the CMORPH precipitation data for daily streamflow simulation and the TRMM and CHIRPS precipitation data for monthly streamflow simulation. This suggests that different precipitation datasets can be used for optimal simulations at different temporal scales.

Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity

Abstract. Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainfall erosivity. As the availability of high temporal resolution rainfall data will most likely not increase in future decades since the monitoring networks have been declining since the 1980s, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored in this study. For this purpose, we used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity. The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. At the continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analyses conducted at the monthly scale for Europe revealed seasonal misalignments, with the occurrence of underestimation of the CMORPH estimates in the summer period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was found for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions, while in high-erosivity regions (> 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (> 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH, which could be regarded as an expected result since this approach indirectly uses the GloREDa data. The application of a simple-linear function correction of the CMORPH data was applied to provide a better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. A preliminary trend analysis of the CMORPH rainfall erosivity estimates was also performed for the 1998–2019 period to investigate possible changes in the rainfall erosivity at a global scale, which has not yet been conducted using high-frequency data such as CMORPH. According to this trend analysis, an increasing and statistically significant trend was more frequently observed than a decreasing trend.

The Diurnal Cycle of Precipitation over Lake Titicaca Basin Based on CMORPH

This paper examines the diurnal cycle of precipitation (DCP) over Lake Titicaca basin (LTb) during the summertime months based on the high spatial–temporal resolution (8 × 8 km2 and hourly) estimates of the Climate Prediction Center Morphing technique (CMORPH). This analysis was carried out using observations from rain gauges (RgSENAMHI) as a reference for the period 2002 to 2013. The accuracy of the CMORPH product was tested with graphical comparisons and several statistical metrics, such as correlation coefficient, bias, and root mean square error. Spatial maps of these metrics and of the diurnal cycle were developed to assess the spatial dependency in the CMORPH accuracy over the LTb. On average, 43% of the total RgSENAMHI variation was explained by the CMORPH. The correlation between the CMORPH and RgSENAMHI amounts was positive over the southeastern and northern LTb and negative in the central and southern LTb. An underestimation bias was observed over most of the LTb areas, and an overestimation bias was observed at some stations (e.g., Lagunillas, Isla Suana, and Desaguadero stations). The total bias decreased when approaching the lake attaining its minimum value over the mountains consistent with previous studies. Overall, the CMORPH was able to capture the spatial patterns of rainfall over the LTb. Over the surrounding lake area, the plateau, and high mountain areas, precipitation peaks were in the late afternoon, while over low areas, such as the valleys and Lake Titicaca, it peaked around midnight to early morning. This result suggests that the DCP is closely related to the local circulation resulting from a response due to solar radiation and the complex orography. On the other hand, the high resolution CMORPH technique can depict finer regional details, such as the less coherent phase pattern over a few regions.

SPATIAL AND TEMPORAL DISTRIBUTION OF RAINFALL EROSIVITY IN BRAZILIAN AMAZONIA: A HYDROLOGICAL PERSPECTIVE BASED ON CMORPH TECHNIQUE

Rainfall erosivity is one crucial factor that controls soil erosion. The interannual variability of rainfall erosivity in Brazilian Amazonia connected to the El Niño-Southern Oscillation. This paper presents a study of the effects of Sea Surface Temperatures anomalies for El Niño region on rainfall erosivity in the Brazilian Amazonia. Rainfall erosivity across was assessed using 30 minutes rainfall data from Climate Prediction Center MORPHing technique during the period from 1998 to 2019. Results indicated that average rainfall erosivity is stronger during La Niña events and weaker during El Niño events. The annual value of rainfall erosivity ranged from 8241 to 24310 (Mj mm ha−1 h−1 year−1). Results also indicated that the effects of extending northward over Brazilian Amazonia showed a significant correlation (P <0.05) between annual rainfall erosivity and annual precipitation. The El Niño-Southern Oscillation conditions was determined to exert the most substantial influence on rainfall erosivity. This information would be useful in the implementation of new soil conservation strategies.

Comparison between Satellite-derived Rainfall and Rain Gauge Observation over Peninsular Malaysia

Validation of the bias-corrected product of National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre Morphing Technique CMORPH-CRT was conducted using gridded rain gauge dataset of Wong et al. (2011) and rain gauge data from meteorological stations throughout Peninsular Malaysia. The CMORPH-CRT was compared for four contrasting topographic sub-regions of Peninsular Malaysia, i.e. west coast (WC), foothills of Titiwangsa range (FT), inland-valley (IN) and east coast (EC). CMORPH-CRT product with grid resolution of 8 km × 8 km at temporal resolution of 1-hour from 00Z January 1998 to 23Z December 2018 was utilized. The results show that CMORPH-CRT are in agreement with the rain gauge data. The CMORPH-CRT performed best over coastal sub-regions but it underestimated over FT sub-region and overestimated at IN. CMORPH-CRT tend to perform better in moderate rather than heavy rainfall events. For extreme weather events, the CMORPH-CRT had shown capability in observing the formation and decay of low-pressure system in Penang during 4th November 2017 and it is in agreement with rain gauge based SPI index i.e. drought conditions over Peninsular Malaysia.

Local Impacts and Surface Characteristics of Cold Pools Wind Gust Observed from Thunderstorm

Cold pools and associated wind storms are frequent occurrences in Southwestern Nigeria, especially during the early monsoon phase. The associated surface wind gust frequently destroys properties resulting in economic losses. Two case events were investigated in this study; one event occurred in May 2019 and the other occurred in March 2020, both in southwestern Nigeria. The National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Prediction (NCEP)/Climate Prediction Center (CPC) Infrared brightness temperatures and CPC Morphing technique (CMORPH) rainfall products were analysed alongside in-situ observations from the Nigerian Meteorological Agency (NiMET). Other data sources analysed are the National Aeronautics and Space Administration (NASA) Prediction Of Worldwide Energy Resources (POWER) and the World Wide Lightning Location Network (WWLLN). Cold pools were identified in the impacted communities as indicated by surface characteristics investigated from the in-situ observations. There was a sudden change in wind direction, with a simultaneous drop in temperature accompanied by increasing wind speed. Pressure and humidity were observed to change in the same period. Thunderstorms were also present in the impacted communities, as observed by the in-situ observations, in both case events. The presence of lightning as observed by WWLLN agrees with the in-situ thunderstorms. The cloud characteristics showed the presence of cloud shields, by their brightness temperature, over the impacted communities during the period of the cold pools in both case events. The systems were raining at the time of the observations in both cases, consistent with the in-situ thunderstorm observations. The communities were heavily impacted with several properties destroyed in the events. These early monsoon seasonal windstorms require a forecasting tool for their prediction and this study presents an eye-opener for further investigation and innovative research to address the menace.

Hydrometeorological Modeling of Limpopo River Basin in Mozambique with TOPMODEL and Remote Sensing

The Limpopo River basin (LRB) is known for its vulnerability to floods, high rates of evapotranspiration, and droughts that cause significant losses to the local community. The present study aimed to perform simulations of flood events occurring in two Mozambican sub-basins of LRB, namely Chókwè and Xai-Xai from 2000 to 2015 with TOPography-based hydrological MODEL (TOPMODEL) and satellite remote sensing data. As input in TOPMODEL, data from two high-resolution global satellite-based precipitation products: Climate Prediction Center MORPHing technique (CMORPH) and Integrated Multi-Satellite Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), 8-day MOD16 evapotranspiration product and surface runoff data estimated by Global Land Data Assimilation System (GLDAS) were used. The sensitivity tests of TOPMODEL parameters were applied using the Monte Carlo simulation. Calibration and validation of the model were performed by the Shuffled Complex Evolution (SCE-UA) method and were evaluated with the Kling-Gupta Efficiency (KGE) index. The results indicated that simulations with the GPM-IMERG (KGE: 0.59 and 0.65) tended to underestimate the stream flows, while with the CMORPH product the performance was much better (KGE: 0.66 and 0.77) in both sub-basins. Thus, TOPMODEL can help to develop flood monitoring systems from satellite remotely sensed data in similar regions of Mozambique.

Evaluation of Satellite Precipitation Estimates over the South West Pacific Region

Rainfall estimation over the Pacific region is difficult due to the large distances between rain gauges and the high convection nature of many rainfall events. This study evaluates space-based rainfall observations over the South West Pacific Region from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP), the USA National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH), the Climate Hazards group Infrared Precipitation with Stations (CHIRPS), and the National Aeronautics and Space Administration’s (NASA) Integrated Multi-Satellite Retrievals for GPM (IMERG). The technique of collocation analysis (CA) is used to compare the performance of monthly satellite precipitation estimates (SPEs). Multi-Source Weighted-Ensemble Precipitation (MSWEP) was used as a reference dataset to compare with each SPE. European Centre for Medium-range Weather Forecasts’ (ECMWF) ERA5 reanalysis was also combined with Soil Moisture-2-Rain–ASCAT (SM2RAIN–ASCAT) to perform triple CA for the six sub-regions of Fiji, New Caledonia, Papua New Guinea (PNG), the Solomon Islands, Timor, and Vanuatu. It was found that GSMaP performed best over low rain gauge density areas, including mountainous areas of PNG (the cross-correlation, CC = 0.64), and the Solomon Islands (CC = 0.74). CHIRPS had the most consistent performance (high correlations and low errors) across all six sub-regions in the study area. Based on the results, recommendations are made for the use of SPEs over the South West Pacific Region.

Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.