Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia (original) (raw)
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Remote Sensing, 2020
High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantil...
Bias Adjustment of Four Satellite-Based Rainfall Products Using Ground-Based Measurements over Sudan
Water
Satellite-based rainfall estimates (SREs) represent a promising alternative dataset for climate and hydrological studies, where gauge observations are insufficient. However, these datasets are accompanied by significant uncertainties. Therefore, this study aims to minimize the systematic bias of Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Global Precipitation Climatology Project (GPCP) rainfall estimates using a quantile mapping (QM) method with climatic zones (CZs). The adjusted rainfall estimates were evaluated for the period from 2003–2017; data from 2003 to 2016 were used for calibration, and data from 2017 were used for validation. The results revealed significant improvements for the adjusted PERSIANN-CCS, PERSIANN-CDR, CHIRPS, and GPCP monthly time series in terms of all statistical measures and evaluation of ov...
Journal of Hydrometeorology, 2009
Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliabl...
Journal of Geophysical Research: Atmospheres, 2005
Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) is a satellite infrared-based algorithm that produces global estimates of rainfall at resolutions of 0.25°Â 0.25°and a half-hour. In this study the model parameters of PERSIANN are routinely adjusted using coincident rainfall derived from the Tropical Rainfall Measurement Mission Microwave Imager (TMI). The impact of such an adjustment on capturing the diurnal variability of rainfall is examined for the Boreal summer of 2002. General evaluations of the PERSIANN rainfall estimates with/without TMI adjustment were conducted using U.S. daily gauge rainfall and nationwide radar network (weather surveillance radar) 1988 Doppler data. The diurnal variability of PERSIANN rainfall estimates with TMI adjustment is improved over those without TMI adjustment. In particular, the amounts of afternoon and morning maximums in rainfall diurnal cycles improved by 14.9% and 26%, respectively, and the original 2-3 hours of time lag in the phase of diurnal cycles improved by 1-2 hours. In addition, the rainfall estimate with TMI adjustment has higher correlation (0.75 versus 0.63) and reduced bias (+8% versus À11%) at monthly 0.25°Â 0.25°resolution than that without TMI adjustment and consistently shows higher correlation (0.62 versus 0.51) and lower bias (+22% versus À30%) at daily 0.25°Â 0.25°scale. This study provides evidence that the TMI, which measures instantaneous rain rates from the TRMM platform flying on a non-Sun-synchronous orbit, enables PERSIANN to capture more realistic diurnal variations of rainfall. This study also reveals the limitation of current satellite rainfall estimation techniques in retrieving the rainfall diurnal features and suggests that further investigation of precipitation generation in different periods of cloud life cycles might help resolve this limitation.
Journal Of Geophysical Research: Atmospheres, 2017
This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the Integrated Multisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained.
Evaluation of PERSIANN System Satellite–Based Estimates of Tropical Rainfall
Bulletin of the American Meteorological Society, 2000
PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° x 0.25° every half-hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° x 1° daily. The authors' current GOES-IR-TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30°S-30°N, 90°E-30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain-rate estimates agree well with the National Climatic Data Center radar-gauge composite data over Florida and Texas (correlation coefficient p > 0.7). The product also compares well (p ~ 0.77-0.90) with the monthly World Meteorological Organization gauge measurements for 5° x 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° x 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index-based TRMM 3B42 product.
2021
Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IME...
Evaluation of PERSIANN-CCS Rainfall Measurement Using the NAME Event Rain Gauge Network
Journal of Hydrometeorology, 2007
Robust validation of the space–time structure of remotely sensed precipitation estimates is critical to improving their quality and confident application in water cycle–related research. In this work, the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) precipitation product is evaluated against warm season precipitation observations from the North American Monsoon Experiment (NAME) Event Rain Gauge Network (NERN) in the complex terrain region of northwestern Mexico. Analyses of hourly and daily precipitation estimates show that the PERSIANN-CCS captures well active and break periods in the early and mature phases of the monsoon season. While the PERSIANN-CCS generally captures the spatial distribution and timing of diurnal convective rainfall, elevation-dependent biases exist, which are characterized by an underestimate in the occurrence of light precipitation at high elevations ...
Uncertainty Assessments of Satellite Derived Rainfall Products
2016
Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP...
Correcting bias of satellite rainfall data using physical empirical model
Atmospheric Research, 2021
The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards. However, a major challenge in their directuse can be problematic due to measurement error. In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. Physical factors, including topography, season, windspeed and cloud types were accounted for correcting bias. Peninsular Malaysia was used as the case study area. Gridded rainfall, developed from 80 gauges for the period 2000-2018, was used along with physical factors in a two-stage procedure. The model consisted of a classifier to categorise rainfall of different intensity and regression models to predict intensity class of different rainfall amount. An ensemble tree-based learning algorithm, called random forest, was used for classification and regression. The results revealed a big improvement of near-real-time GSMaP_NRT product after bias correction (GSMaP_BC) compared to the gauge corrected version (GSMaP_GC). Accuracy evaluation for complete time series indicated about 110% reduction of normalized root-mean-square error (NRMSE) in GSMaP_BC (0.8) compared to GSMaP_NRT (1.7) and GSMaP_GC (1.75). On the other hand, the bias of GSMaP_BC became nearly zero (0.3) compared to 2.1 and-3.1 for GSMaP_NRT and GSMaP_GC products. The spatial correlation of GSMaP_BC was >0.7 with observed rainfall data for all months compared to 0.2-0.78 for GSMaP_NRT and GSMaP_GC, indicating capability of GSMaP_BC to replicate spatial pattern of rainfall. The bias-corrected near-real-time GSMaP data can be used for monitoring and forecasting floods and hydrological phenomena in the absence of dense rain-gauge network in areas, frequently experience hydro-meteorological hazards.