Bias correction of daily satellite precipitation data using genetic algorithm

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.

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Accelerating assimilation development for new observing systems using EFSO

Nonlinear Processes in Geophysics ◽

10.5194/npg-25-129-2018 ◽

2018 ◽

Vol 25 (1) ◽

pp. 129-143 ◽

Cited By ~ 4

Author(s):

Guo-Yuan Lien ◽

Daisuke Hotta ◽

Eugenia Kalnay ◽

Takemasa Miyoshi ◽

Tse-Chun Chen

Keyword(s):

Selection Criteria ◽

Ensemble Forecast ◽

Data Selection ◽

Development Work ◽

Observation Data ◽

Precipitation Data ◽

Trial And Error ◽

Satellite Precipitation ◽

Selection Strategies ◽

Observing Systems

Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

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Application of TRMM Precipitation Data to Evaluate Drought and Its Effects on Water Resources Instability

Applied Sciences ◽

10.3390/app9245377 ◽

2019 ◽

Vol 9 (24) ◽

pp. 5377

Author(s):

Ata Amini ◽

Abdolnabi Abdeh Kolahchi ◽

Nadhir Al-Ansari ◽

Mehdi Karami Moghadam ◽

Thamer Mohammad

Keyword(s):

Water Resources ◽

Standardized Precipitation Index ◽

Remote Sensing Data ◽

Tropical Rainfall Measuring Mission ◽

Monthly Precipitation ◽

Precipitation Data ◽

Discharge Data ◽

Satellite Precipitation ◽

Discharge Rates ◽

Trmm Precipitation

The present research was carried out to study drought and its effects upon water resources using remote sensing data. To this end, the tropical rainfall measuring mission (TRMM) satellite precipitation, the synoptic stations, and fountain discharge data were employed. For monitoring of drought in the study area, in Kermanshah province, Iran, the monthly precipitation data of the synoptic stations along with TRMM satellite precipitation datasets were collected and processed in the geographic information system (GIS) environment. Statistical indicators were applied to evaluate the accuracy of TRMM precipitation against the meteorological stations’ data. Standardized precipitation index, SPI, and normalized fountain discharge were used in the monitoring of drought conditions, and fountains discharge, respectively. The fountains were selected so that in addition to enjoying the most discharge rates, they spread along the study area. The evaluation of precipitation data showed that the TRMM precipitation data were of high accuracy. Studies in temporal scale are indicative of the strike of drought in this region to the effect that for most months of the year, frequency and duration in dry periods are much more than in wet periods. As for seasonal scales, apart from winter, the frequency and duration of drought in spring and autumn have been longer than in wet years. Moreover, the duration of these periods was different. A comparison between the results of changes in fountain discharges and drought index in the region has verified that the drought has caused a remarkable decline in the fountain discharges.

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Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Water ◽

10.3390/w10081006 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1006 ◽

Cited By ~ 4

Author(s):

Xiuna Wang ◽

Yongjian Ding ◽

Chuancheng Zhao ◽

Jian Wang

Keyword(s):

Bias Correction ◽

Arid Region ◽

Rain Gauge ◽

Climatic Conditions ◽

Monthly Precipitation ◽

Observation Data ◽

Precipitation Data ◽

Rain Gauges ◽

Hexi Region ◽

Precipitation Events

Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

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Evaluation of Satellite Based Precipitations and Their Applicability for Rainfall Runoff Modelling in Narayani Basin of Nepal

Journal of Hydrology and Meteorology ◽

10.3126/jhm.v8i1.15569 ◽

2016 ◽

Vol 8 (1) ◽

pp. 22-31 ◽

Cited By ~ 1

Author(s):

Sunil Ghaju ◽

Knut Alfredsen

Keyword(s):

Bias Correction ◽

Rain Gauge ◽

Extensive Study ◽

Rainfall Runoff ◽

Runoff Simulation ◽

Satellite Precipitation ◽

Precipitation Estimates ◽

Set Up ◽

Rain Gauge Network ◽

Precipitation Station

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31

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Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change

Water ◽

10.3390/w11071475 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1475 ◽

Cited By ~ 7

Author(s):

Jun-Haeng Heo ◽

Hyunjun Ahn ◽

Ju-Young Shin ◽

Thomas Rodding Kjeldsen ◽

Changsam Jeong

Keyword(s):

Climate Change ◽

Bias Correction ◽

Shape Parameter ◽

Mapping Method ◽

Distribution Model ◽

Parameter Distribution ◽

Precipitation Data ◽

Distribution Models ◽

Quantile Mapping

The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.

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Evaluation of bias-adjusted satellite precipitation estimations for extreme flood events in Langat river basin, Malaysia

Hydrology Research ◽

10.2166/nh.2019.071 ◽

2019 ◽

Vol 51 (1) ◽

pp. 105-126 ◽

Cited By ~ 1

Author(s):

Eugene Zhen Xiang Soo ◽

Wan Zurina Wan Jaafar ◽

Sai Hin Lai ◽

Faridah Othman ◽

Ahmed Elshafie ◽

...

Keyword(s):

River Basin ◽

Bias Correction ◽

Monsoon Season ◽

Rainfall Threshold ◽

Careful Consideration ◽

Northeast Monsoon ◽

Satellite Precipitation ◽

Extreme Floods ◽

Extreme Flood ◽

Langat River

Abstract Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

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Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

Monthly Weather Review ◽

10.1175/mwr-d-17-0125.1 ◽

2017 ◽

Vol 145 (12) ◽

pp. 4997-5014 ◽

Cited By ~ 11

Author(s):

Liao-Fan Lin ◽

Ardeshir M. Ebtehaj ◽

Alejandro N. Flores ◽

Satish Bastola ◽

Rafael L. Bras

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Specific Humidity ◽

Surface Model ◽

Precipitation Data ◽

Near Surface ◽

Satellite Precipitation ◽

Soil Moisture Data ◽

Mean Square Errors

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

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