Climate Prediction Center Morphing Technique Research Articles

Accuracy analysis of IMERG and CMORPH precipitation data over North China

Large-scale agricultural production in North China makes the study of precipitation in this area vital. The performance of the Integrated Merged Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) and the Climate Prediction Center morphing technique (CMORPH) precipitation products for 2015 was evaluated against daily precipitation data from 404 rain gauges in North China. Relative errors, correlation coefficients, Pearson’s chi-squared test values, and root mean square errors, as well as the probability of detection (POD), false alarm ratio, and critical success index, were used to analyze the accuracy of both IMERG and CMORPH precipitation products on daily, monthly, and seasonal timescales. The probability density function (PDF) was also considered. Overall, both products overestimated ground precipitation, especially in summer. Positive correlation coefficients between satellite-derived and rain-gauge monthly precipitation data were higher over plains and coastal areas, compared with plateau regions. The PODs of both IMERG and CMORPH data were highest in summer. The PODs of IMERG data were much higher than for CMORPH data in autumn. The PODs over coastal regions, plains, and plateaus at lower latitudes also were considerably better than over inland and plateau areas at higher latitudes. The precipitation products performed best over coastal areas, plains, and areas with high rainfall. Both CMORPH and IMERG products were prone to identifying non-rainy days as rainy days. They also overestimated light (0.1-9.9 mm d-1) and moderate (10-24.9 mm d-1) precipitation events, although the IMERG product was more sensitive to precipitation. Accordingly, we find that both of these satellite-derived precipitation products require further modification to enable them to substitute for gauge precipitation data in North China.

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS), we blended the Climate Prediction Center (CPC) morphing technique (CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China (CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2 (GLDAS-V2.1) precipitation, and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement (GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

Precipitation Diurnal Cycle Assessment of Satellite-Based Estimates over Brazil

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

Streamflow simulations using error correction ensembles of satellite rainfall products over the Huaihe river basin

This study aims to investigate the hydrologic applicability of an error correction method – SREM2D (two-dimensional stochastic satellite rainfall error model) to three satellite precipitation products in streamflow simulations. Three satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multiple-Satellite Precipitation Analysis (TMPA) real-time 3B42 product (3B42RT), the Climate Prediction Centre (CPC) morphing technique (CMORPH) gauge merged product (CMORPH BLD), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network – Climate Data Record (PERSIANN CDR), are corrected using SREM2D. Over the upper Huaihe river basin, streamflow ensemble simulations are derived by forcing the distributed Variable Infiltration Capacity (VIC) model with the SREM2D-based rainfall ensemble.After applying SREM2D to satellite precipitation products, the streamflow simulations forced by TMPA 3B42RT and PERSIANN CDR rainfall ensembles are capable to capture flood peaks. However, the streamflow simulations forced by CMORPH BLD rainfall ensemble show poor performance for the extreme events, but exhibit good accuracy in non-flood flow simulation. The calibration of the model over the headwater subbasin betters the streamflow simulation, especially for reproducing extreme events during the main flood cases. Overall, SREM2D provides great potential to facilitate the application of satellite precipitation products in water management and decision making over Chinese river basins.

Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan

Ensemble multi-satellite precipitation datasets (ESPDs) are alternative to satellite-based precipitation products (SPs), which tend to reduce the errors, combine advantages of individual SPs, and have higher accuracy for hydrological applications. The current study proposes and evaluates a dynamic WALS-ESPD developed using the Weighted Average Least Square (WALS) algorithm, which has 0.25° spatial and daily temporal resolutions across glacial, humid, arid and hyper-arid regions of Pakistan during 2000–2015. WALS-ESPD is developed using three SPs, 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 one re-analysis product, Era-Interim. Mean Bias (MB), Mean Absolute Error (MAE), unbiased Root Mean Square Error (ubRMSE), Correlation Coefficient (R), Kling-Gupta efficiency (KGE score), and Theil's U are used to evaluate the performance of WALS-ESPD both spatially and temporally. Moreover, the skill scores of statistical metrics are used to assess the WALS-ESPD performance against two previously developed ESPDs, DBMA-ESPD and DCBA-ESPD. TMPA dominated all SPs with average weights of 0.317, 0.341, 0.314, and 0.326 across the glacial, humid, arid and hyper-arid regions. TMPA dominated pre-monsoon (30.26%) and monsoon (35.82%) seasons, while PERSIANN-CDR dominated post-monsoon (27.58%) and winter (29.82%) seasons. WALS-ESPD performed relatively poor across the glacial and humid regions, and during monsoon and pre-monsoon seasons. Skill scores of WALS-ESPD against DBMA-ESPD show better performance of WALS-ESPD in all four regions, especially across the glacial region with the maximum MB, MAE, and ubRMSE scores of 27.36%, 28.34%, and 27.67%, respectively. Meanwhile, WALS-ESPD performed better than DCBA-ESPD in the whole glacial region and most part of other regions, while DCBA-ESPD dominated WALS-ESPD at few stations across humid, arid, and hyper-arid (south-east) regions.

Post and near real-time satellite precipitation products skill over Karkheh River Basin in Iran

ABSTRACT Due to high spatial and temporal resolution and near real-time accessibility of satellite precipitation data, the necessity of using these data in the hydrological application seems to be more pressing than ever. In this study, the skill of six post real-time (Climate Hazards Group Infrared Precipitation with Station data (CHIRPS); CPC MORPHing technique (CMORPH); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); PERSIANN Climate Data Record (PERSIANN-CDR); precipitation produced from the inversion of the satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (SM2RAIN); Tropical Rainfall Measuring Mission (TRMM 3B42-V7)) and two near real-time (PERSIANN Cloud Classification System (PERSIANN-CCS); TRMM real-time (TRMM 3B42-RT)) satellite daily precipitation products are evaluated by comparing with 28 rain gauges in Karkheh River Basin, located in the semi-arid region of Iran. The evaluation is performed for two types of quantiles (lower quantile (< Q10 and < Q25) and upper quantile (> Q50, > Q75, and > Q95)) and rainy seasons using categorical and quantitative metrics for the period March 2003 to December 2014. The spatial analysis indicated that there is not remarkable variation in the skill of satellite precipitation products across the study area. Results showed that the satellite precipitation estimates are more accurate in lower than upper quantile. The seasonal analysis presented that the skill of satellite precipitation products for fall and spring is slightly higher than winter. For post real-time satellite, in terms of POD (VHI), PERSIANN-CDR in spring (winter and spring), SM2RAIN in winter and spring (fall) shows the best skill, and according to FAR and CSI, CMORPH is the best in all seasons. In addition, VHI and POD of PERSIANN-CCS have better skill than 3B42-RT for near real-time satellite for all seasons. Generally, PERSIANN-CCS (PERSIANN-CDR and SM2RAIN) shows the best skill for near (post) real-time satellite precipitation estimations when whole data are included in the analysis.

Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin

Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions—all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998–2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.

Inovasi Model Prediksi Titik Api Menggunakan Consecutive Dry Days (CDD) pada Wilayah Lahan Gambut Provinsi Riau

Riau Province, which has a peatland area of ​​5.09 million hectares or 56.42% of the area of ​​peatlands on the island of Sumatra, is one of the provinces in Indonesia which has the greatest potential for forest fires to devastate hundreds of hectares of forest and gardens and increase home gas emissions glass in the atmosphere. Forest fires that occur are closely related to Consecutive Dry Days (CDD) or consecutive days without rain (HTH) which result in low humidity, thereby affecting the potential for hotspots to emerge, especially around peatlands. CDD, which is the result of the accumulation of the amount of HTH in one period between rainy day events, is calculated using CMORPH precipitation data (CPC MORPHing technique). Hotspot data is obtained from NOAA and Hotspot event data with a confidence level of more than 70% in the eastern region of Riau province where there is a peatland. The probability of a Hotspot occurring is calculated based on the CDD for each grid that will produce a CDD value with a significant probability for the occurrence of a Hotspot on the grid each month. The level of density of Hotspots in each season period with a high category is in the JJA and MAM periods. An early warning system for the emergence of a Hotspot based on the level of Hotspot and CDD density is carried out the day before the opportunity arises with a value less than or equal to 0.5. With an average CDD value for early warning in MAM is 13 days and in JJA is eight days. This research is expected to be able to be applied in early warning of forest and land fires in Riau.

Assessment of high-resolution satellite rainfall products over a gradually elevating mountainous terrain based on a high-density rain gauge network

ABSTRACT High-resolution satellite rainfall products (SRPs) provide forcing inputs for hydrologic applications. Complex mountainous terrains have a significant effect on the occurrence and intensity of rainfall. This study focuses on the assessment of errors and rainfall detection capability of SRPs over a complex terrain with an elevation ranging from −95 to 3091 m based on a high-density rain gauge network over the Taihang Mountains of North China. The performance of four high-resolution SRPs (rain gauge bias-corrected Climate Prediction Center morphing technique (CMORPH CRT), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) 3B42V7; and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS)) was validated using 104 rain gauge stations from 1 January 2016 to 31 December 2017, and the results show that the annual rainfall varied from 375 mm to 1400 mm on average in 2016 and 2017. At the monthly scale, all SRPs except PERSIANN-CCS performed well. The spatial pattern of the annual rainfall variation, which was highest in the southeast and lowest in the northwest, was adequately captured by IMERG and 3B42 but not CMORPH CRT and PERSIANN-CCS. As indicated by the statistical metrics, all SRPs except PERSIANN-CCS exhibited better performance in the regions in the downward direction of the East-Asian Monsoon. In terms of rainfall detection, all SRPs exhibited moderate rainfall detection capability while IMERG exhibited the lowest false alarm ratio (FAR) equal to 0.41. Compared with 3B42, a significant improvement was found in IMERG, which presented increased correlation coefficient (r) and decreased FAR values over the study areas, and the improvement rate was 75% and 95%, respectively. All SRPs underestimated the no/light rainfall (0–1 mm day−1) events. IMERG and PERSIANN-CCS exhibited poor performance with significant underestimation of the 1–2 mm day−1 rainfall class and overestimation of the 2–5 mm day−1 rainfall class. Our results not only demonstrate the superiority of different products at different elevations but also provide suggestions for further improvement of the SRPs, especially for complex terrains.

Performance of the CMORPH and GPM IMERG Products over the United Arab Emirates

Satellite-based precipitation products are becoming available at very high temporal and spatial resolutions, which has accelerated their use in various hydro-meteorological and hydro-climatological applications. Because the quantitative accuracy of such products is affected by numerous factors related to atmospheric and terrain properties, validating them over different regions and environments is needed. This study investigated the performance of two high-resolution global satellite-based precipitation products: the climate prediction center MORPHing technique (CMORPH) and the latest version of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), V06, over the United Arab Emirates from 2010 through 2018. The estimates of the products and that of 71 in situ rain gauges distributed across the country were compared by employing several common quantitative, categorical, and graphical statistical measures at daily, event-duration, and annual temporal scales, and at the station and study area spatial scales. Both products perform quite well in rainfall detection (above 70%), but report rainfall not observed by the rain gauges at an alarming rate (more than 30%), especially for light rain (lower quartile). However, for moderate and intense (upper quartiles) rainfall rates, performance is much better. Because both products are highly correlated with rain gauge observations (mostly above 0.7), the satellite rainfall estimates can probably be significantly improved by removing the bias. Overall, the CMORPH and IMERG estimates demonstrate great potential for filling spatial gaps in rainfall observations, in addition to improving the temporal resolution. However, further improvement is required, regarding the overestimation and underestimation of small and large rainfall amounts, respectively.

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.

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 (&lt;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.

Reliability of Gridded Precipitation Products in the Yellow River Basin, China

Gridded precipitation products are the potential alternatives in hydrological studies, and the evaluation of their accuracy and potential use is very important for reliable simulations. The objective of this study was to investigate the applicability of gridded precipitation products in the Yellow River Basin of China. Five gridded precipitation products, i.e., Multi-Source Weighted-Ensemble Precipitation (MSWEP), CPC Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), were evaluated against observations made during 2001−2014 at daily, monthly, and annual scales. The results showed that MSWEP had a higher correlation and lower percent bias and root mean square error, while CMORPH and GSMaP made overestimations compared to the observations. All the datasets underestimated the frequency of dry days, and overestimated the frequency and the intensity of wet days (0–5 mm/day). MSWEP and TRMM showed consistent interannual variations and spatial patterns while CMORPH and GSMaP had larger discrepancies with the observations. At the sub-basin scale, all the datasets performed poorly in the Beiluo River and Qingjian River, whereas they were applicable in other sub-basins. Based on its superior performance, MSWEP was identified as more suitable for hydrological applications.

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).

INFLUÊNCIA DE SISTEMAS PRECIPITANTES SOBRE A PRODUÇÃO DE SERAPILHEIRA EM MANGUEZAL DA COSTA AMAZÔNICA

Manguezais são ecossistemas com significativa relevância ambiental e socioeconômica,e o conhecimento da relação ecológica desse ecossistema com a atmosfera é defundamental importância para sua preservação e para o uso sustentável dos recursosnaturais. Diante disso, este estudo tem como objetivos analisar os sistemas meteorológicosprecipitantes e sua variação pluviométrica sazonal sobre a costa amazônica, bem comoexaminar sua influência na variabilidade da produção de serapilheira de floresta demangue. Delimitaram-se três parcelas amostrais com 12 coletores de serapilheira de 1 m2no manguezal do Sítio Experimental de Cuiarana. Classificaram-se as coletas realizadasmensalmente em folha, lenhoso, material reprodutivo e miscelânea. Utilizaram-sedados de precipitação pluvial com base na Torre Micrometeorológica e na técnicaCPC MORPHing Technique (CMORPH). Utilizaram-se as análises two-way e one-wayde variância (ANOVA) (p &lt; 0,05) e análise de regressão linear (p &lt; 0,05). Os resultadosindicam que a variação temporal da precipitação é modulada por diferentes sistemasmeteorológicos. A produção total anual de serapilheira foi de 9,4 ± 0,06 Mg ha-1 ano-1,da qual 67% compunha-se de fração folha. As análises da fração folha total e folha daespécie L. racemosa apresentaram correlação significativa com a precipitação (r2 = 0,36e r2 = 0,72; p &lt; 0,05), respectivamente. Diante dos resultados, conclui-se que a produçãode serapilheira do manguezal possui direta relação com os sistemas precipitantes.

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.

Modeling of the spatial and temporal dynamics of erosivity in the Amazon

Precipitation is one of the main factors of soil erosion, and the intensity, duration and frequency of precipitation can aggravate the erosive process. The objective of the study was to determine the spatial and temporal distribution of rainfall erosivity in the Amazon. Data were collected from 334 rainfall gauge stations distributed throughout the region, composing a series of 20 years (1997–2016). The gaps in this series were filled with satellite data using the CPC MORPHing technique. The erosivity equations used were those available in the literature and based on the modified Fournier index. The values of precipitation and erosivity were interpolated using GIS software, and thematic maps were generated for these variables. The annual value of rainfall erosivity ranged from 7060 to 36767 (Mj mm ha−1 h−1 year−1). On the monthly scale, the highest rates of erosivity were recorded in the rainy season, i.e., February and March, at approximately 1548 and 2651 (Mj mm ha−1 h−1 month−1), respectively. In the context of erosion risk, the region was classified as having very strong erosivity. Therefore, it is imperative that land management and conservation policies be implemented to minimize erosion in the region, which, at its borders, undergoes intense land use change, i.e., forests are being transformed into pastures and grain crops.

A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations

Space-based precipitation products are often used for regional and/or global hydrologic modeling and climate studies. A number of precipitation products at multiple space and time scales have been developed based on satellite observations. However, their accuracy is limited due to the restrictions on spatiotemporal sampling of the satellite sensors and the applied parametric retrieval algorithms. Similarly, a ground-based weather radar is widely used for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization capability and urban scale deployment of high-resolution X-band radar networks. Ground-based radars are often used for the validation of various spaceborne measurements and products. This article introduces a novel machine learning-based data fusion framework to improve the satellite-based precipitation retrievals by incorporating dual-polarization measurements from a ground radar network. The prototype architecture of this fusion system is detailed. In particular, a deep learning multi-layer perceptron (MLP) model is designed to produce the rainfall estimates using the geostationary satellite infrared (IR) data and low earth orbit satellite passive microwave (PMW)-based retrievals as inputs. The high-quality rainfall products from the ground radar network are used as the target labels to train this MLP model. An urban scale demonstration study over the Dallas-Fort Worth (DFW) metroplex is presented. In addition, the Climate Prediction Center morphing technique (i.e., CMORPH) is adopted for preprocessing of the satellite observations. Rainfall products from this deep learning system are evaluated using the standard CMORPH products. The results show that the proposed data fusion framework can be used for generating accurate precipitation estimates and could be considered as an alternative tool for developing future satellite retrieval algorithms.

Evaluation of satellite-based precipitation estimates over Algeria during 1998–2016

This study concentrated on the assessment of two operational high-resolution satellite-based rainfall products, Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and NOAA CPC Morphing Technique (CMORPH) over Algeria. The assessment was carried out for a duration of 19-years ranging between 1998 and 2016 using 20 rain gauge datasets. The study area can be divided into five regions (zones) according to Köppen climate classification (CSA, BSK, BWK, BSH, and BWH). Both satellite derived rainfall estimates (SRE); (CHIRPS and CMORPH) were evaluated over these regions. Continuous statistics was employed to measure their performance in evaluating and reproducing rainfall while categorical statistics employed on daily time scale to assess the ability of SRE in detecting rain/no rain events. Correlation Coefficient (R), Bias and Root Mean Square Error (RMSE) are employed on daily, monthly as well as on annual timescales. At daily scale, most SRE showed unsatisfactorily performance over all the five regions with exception for CMORPH products exhibiting better performance. However, as the time step augmented, the performance of SRE enhanced. At monthly time scale a higher agreement was observed. The CHIRPS performed the best (R = 0.9), whereas CMORPH-CRTv1.0 showed relatively weaker but good correlation (R = 0.83). At annually time scale CHIRPS has a better performance in CSA, BSK regions, on the other hand CMORPH demonstrate a better results BSH, BWK, BWH regions. The study demonstrate that reliable evaluation of rainfall in Algeria with different climate zones employing satellite-based precipitation estimation products remains a challenge.

Evaluation of Precipitation Estimates CMORPH-CRT on Regions of Mexico with Different Climates

Satellite-based precipitation (SBP) products with global coverage have the potential to overcome the lack of information in places where there are no rain gauges to perform hydrological analyses; however, it is necessary to evaluate the reliability of the SBP products. In this study, we evaluated the performance of the Climate Prediction Center morphing technique with corrected bias (CMORPH-CRT) product in 14 sites in Mexico. The evaluation was carried out using two approaches: (1) using categorical metrics that include indicators of probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and frequency bias index (FBI); and (2) through statistical indicators such as the mean absolute error (MAE), root mean square error (RMSE), relative bias (RB), and correlation coefficient (CC). The analysis was carried out with two levels of temporal aggregation: 30 min and daily. The results indicate that the CMORPH-CRT product overestimates the number of precipitation events in most cases since FBI values greater than 1 in 78.6% of analyzed stations were obtained. Also, we obtained CC values in the range of 0.018 to 0.625, which implied weak to moderate correlations, and found that in all stations, the CMORPH-CRT product overestimates the precipitation (RB &gt; 0).