scholarly journals A flexible two-stage approach for blending multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

2020 ◽  
Author(s):  
Yingzhao Ma ◽  
Xun Sun ◽  
Haonan Chen ◽  
Yang Hong ◽  
Yinsheng Zhang
Keyword(s):  
Tibetan Plateau ◽  
Complex Terrain ◽  
Warm Season ◽  
Regression Function ◽  
Rain Gauge ◽  
Model Parameters ◽  
Satellite Precipitation ◽  
Northeastern Tibetan Plateau ◽  
Blending Method ◽  
Precipitation Estimates

Abstract. Substantial biases exist in the Satellite Precipitation Estimates (SPE) over complex terrain regions and it has always been a challenge to quantify and correct such biases. The combination of multiple SPE and ground observations would be beneficial to improve the precipitation estimates. In this study, a flexible two-step approach is proposed by firstly reducing the systematic errors of each SPE using rain gauge observations as references, and then merging the improved multi-SPE with a Bayesian weighting model. In the 1st stage, gauge references are assumed as a generalized regression function of SPE and terrain feature. In the 2nd stage, the weights assigned to the involved SPE are calculated according to the associated performance relative to gauge references. This blending method has the ability to exert benefits from multi-SPE in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying prior distributions on the model parameters, which enables to produce posterior ensembles associated with their predictive uncertainties. The performance of the two-step blending approach is assessed using independent rain gauge observations during the warm season of 2014 in the northeastern Tibetan Plateau. Results show that the blended multi-SPE are significantly improved compared to the original individuals, especially during heavy rainfall events. This study can also be expanded as a data fusion framework in the development of high-quality precipitation products in high-cold regions characterized by complex terrain.

scholarly journals A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

2021 ◽  
Vol 25 (1) ◽  
pp. 359-374
Author(s):  
Yingzhao Ma ◽  
Xun Sun ◽  
Haonan Chen ◽  
Yang Hong ◽  
Yinsheng Zhang
Keyword(s):  
Tibetan Plateau ◽  
Warm Season ◽  
Regression Function ◽  
Rain Gauge ◽  
Model Parameters ◽  
Two Stage ◽  
Validation Data ◽  
Satellite Precipitation ◽  
Northeastern Tibetan Plateau ◽  
Precipitation Estimates

Abstract. Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.

scholarly journals Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau

2018 ◽  
Vol 10 (8) ◽  
pp. 1316 ◽  
Author(s):  
Peng Bai ◽  
Xiaomang Liu
Keyword(s):  
Tibetan Plateau ◽  
Yellow River ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Distributed Hydrological Model ◽  
Satellite Precipitation ◽  
Rain Gauges ◽  
Upper Yangtze River ◽  
Precipitation Estimates

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture.

scholarly journals Improving satellite precipitation estimates over complex terrain through the use of high-resolution numerical weather predictions

2018 ◽  
Author(s):  
Νικόλαος Μπαρτσώτας
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Satellite Precipitation ◽  
Numerical Weather ◽  
Precipitation Estimates

Ο υετός αποτελεί θεμελιώδη παράμετρο για ένα ευρύτατο φάσμα ανθρώπινων δραστηριοτήτων. Τόσο η έλλειψη όσο και οι υπερβολικές του ποσότητες προκαλούν σημαντικές συνέπειες και απειλούν ανθρώπινες ζωές και υποδομές. Η αβεβαιότητα που εξακολουθεί να υπάρχει στην πρόγνωση και επισκόπησή του, έχει σημαντικότατες προεκτάσεις στην γεωργία, τις μεταφορές, την αξιοποίηση υδάτινων πόρων καθώς και την παραγωγή ενέργειας από ανανεώσιμες πηγές. Σε ακραίες εκδοχές φαινομένων υετού, όπως οι πολύ ισχυρές καταιγίδες που συνοδεύονται από ηλεκτρικά φαινόμενα, η αβεβαιότητα αυτή καθίσταται ισχυρότερη. Αυτού του είδους οι καταιγίδες αναπτύσσονται σε πολύ μικρές χωρικές και χρονικές κλίμακες, χαρακτηριστικό το οποίο ανάγει την πρόγνωσή τους σε ιδιαίτερα απαιτητική διαδικασία.Η απαραίτητη πληροφορία είναι επί του παρόντος αδύνατον να προκύψει από μία και μόνο πηγή μέτρησης ή έμμεσης εκτίμησης του υετού, καθώς έκαστη συνοδεύεται από συγκεκριμένους περιορισμούς. Καθίστανται έτσι επιτακτική η ανάγκη προς μια συνδυαστική προσέγγιση. Η συγκεκριμένη διδακτορική διατριβή συνεισφέρει στη δημιουργία καλύτερων εκτιμήσεων υετού πάνω από περιοχές έντονου αναγλύφου, συνδυάζοντας αποτελεσματικά τα επιμέρους θετικά των διαθέσιμων πηγών πληροφορίας. Μετρήσεις από όργανα τηλεπισκόπησης (μετεωρολογικά ραντάρ και δορυφόροι), παρατηρήσεις από δίκτυα βροχομέτρων και ένα πλήθος αριθμητικών μοντέλων πρόγνωσης (ατμοσφαιρικό, υδρολογικό, μοντέλο διάχυσης σωματιδίων) επιστρατεύονται προς αυτό το σκοπό.Μια νέα τεχνική προσαρμογής δορυφορικών μετρήσεων αναπτύχθηκε στα πλαίσια αυτής της διατριβής. Σε αυτή, τα δορυφορικά δεδομένα αξιοποιούνται ως προς την εκτίμηση της χωροχρονικής εξέλιξης των καταιγίδων, ενώ σε ότι αφορά την ποσότητα του υετού, οι εκτιμήσεις προσαρμόζονται στις αντίστοιχες του αριθμητικών μοντέλων πρόγνωσης. Κατ’ αυτόν τον τρόπο, η αξιόπιστη χωροχρονική επισκόπηση από τους δορυφόρους διατηρείται ενώ οι συχνά εσφαλμένες ποσότητες υετού των δορυφορικών οργάνων πάνω από ορεινές περιοχές διορθώνονται με τη χρήση των ατμοσφαιρικών προσομοιώσεων. Η διόρθωση των δορυφορικών δεδομένων λαμβάνει χώρα μέσω μιας μεθόδου πυκνότητας πιθανότητας. Η αξιολόγηση των πρωτογενών δορυφορικών δεδομένων, των αριθμητικών προσομοιώσεων και των τελικών υβριδικών προϊόντων γίνεται έναντι σε πυκνά δίκτυα βροχομέτρων και πεδία από διαθέσιμα μετεωρολογικά ραντάρ. Λαμβάνει δε χώρα σε τρεις ορεινές περιοχές με διαφορετικά χαρακτηριστικά: δύο μέσων γεωγραφικών πλατών (Άλπεις και Βραχώδη Όρη) και μια υποτροπική (Αιθιοπία).Οι προσομοιώσεις των αριθμητικών μοντέλων υποδεικνύουν τη φύση των περιορισμών στην ανίχνευση του υετού από τα δορυφορικά όργανα. Μια μικροφυσική διερεύνηση λαμβάνει χώρα και οι ομοιότητες που παρουσιάζουν οι εν λόγω καταιγίδες στις περιπτώσεις όπου η δορυφορική ανίχνευση εμφανίζει μεγάλες αποκλίσεις από τις παρατηρήσεις σχολιάζονται διεξοδικά. Παράλληλα, παρουσιάζεται μια εκτίμηση του οφέλους που μπορεί να προκύψει στο άμεσο μέλλον από την υιοθέτηση πολύ λεπτομερών χωρικών αναλύσεων στα αριθμητικά μοντέλα πρόγνωσης. Αποτελέσματα από προσομοιώσεις σε χωρικές κλίμακες μικρότερες του 1 χιλιομέτρου (σ.σ.: έως και 250 μέτρα) συγκρίνονται με αντίστοιχα από κλίμακες που αποτελούν τον τρέχοντα κανόνα στις μετεωρολογικές υπηρεσίες (1 και 4 χιλιόμετρα). Οι επιπτώσεις που προκαλούν αυτές οι διαφορές στην εκτίμηση του υετού από το ατμοσφαιρικό μοντέλο στην υδρολογία και συγκεκριμένα στην απορροή των υδάτων εξετάζονται μέσω αντίστοιχων προσομοιώσεων με υδρολογικό μοντέλο.Για τις ανάγκες της διατριβής χρησιμοποιήθηκαν ένα εξελιγμένο ατμοσφαιρικό αριθμητικό μοντέλο (RAMS/ICLAMS), ένα υδρολογικό μοντέλο (CREST) καθώς κι ένα λανγκρανζιανό μοντέλο διασποράς-διάχυσης (HYPACT). Το πρώτο καθόρισε την υετίσιμη ποσότητα σε κάθε καταιγίδα και παρείχε την πληροφορία για περαιτέρω ανάλυση σε επίπεδο μικροφυσικής νεφών, το δεύτερο εκτίμησε τις απορροές που προέκυψαν από τις ατμοσφαιρικές προσομοιώσεις και το τρίτο χρησίμευσε στον καθορισμό της προέλευσης των υγρών αερίων μαζών πάνω από περιοχές όπου η βιβλιογραφία δεν ήταν ιδιαίτερα εκτεταμένη. Δυο δορυφορικά προϊόντα, που βασίζονται σε διαφορετικές τεχνικές ανίχνευσης και συγκεκριμένα από αισθητήρες υπέρυθρου (IR) και μικροκυμάτων (PMW) χρησιμοποιήθηκαν προκειμένου να υποδείξουν τους περιορισμούς που χαρακτηρίζουν την κάθε μέθοδο ανίχνευσης πάνω από περιοχές έντονου αναγλύφου. Αμφότερα είναι προϊόντα υψηλής χωρικής ανάλυσης (4 και 8 χιλιόμετρα αντίστοιχα).Τα αποτελέσματα εμφανίζουν οφέλη από τις λεπτομερείς χωρικές κλίμακες των προσομοιώσεων, τόσο στις ποσότητες του υετού, στη λεπτομερέστερη χωρική του κατανομή, όσο και την ακριβέστερη εκτίμηση της απορροής στη συνέχεια. Οι δορυφορικές μετρήσεις εμφανίζουν μια ξεκάθαρη τάση υποεκτίμησης του υετού πάνω από περιοχές έντονου αναγλύφου. Τα διορθωμένα δορυφορικά προϊόντα που προέκυψαν από την προτεινόμενη μέθοδο, υπερτερούν έναντι των πρωτογενών στη στατιστική ανάλυση και στις δύο περιοχές εφαρμογής. Σε επίπεδο μικροφυσικών ομοιοτήτων μεταξύ των περιπτώσεων ανεπαρκούς ανίχνευσης από τα δορυφορικά όργανα, παρατηρήθηκαν μικρές συγκεντρώσεις σωματιδίων πάγου και νεφικοί σχηματισμοί με περιορισμένη κατακόρυφη ανάπτυξη. Η διόρθωση των δορυφορικών παρατηρήσεων μέσω των αριθμητικών προσομοιώσεων εμφανίζεται ως μια αξιόπιστη εναλλακτική σε περιοχές όπου οι παρατηρήσεις δεν είναι επαρκείς προς εξυπηρέτηση αυτού του σκοπού.Η συνεισφορά της παρούσης διατριβής έγκειται αφενός στην προετοιμασία του εδάφους για μελλοντικά υβριδικά προϊόντα υετού, αφετέρου στην ανίχνευση των μικροφυσικών ομοιοτήτων που εμφανίζουν οι καταιγίδες οι οποίες δεν ανιχνεύονται ικανοποιητικά από τα δορυφορικά όργανα. Το τελευταίο μπορεί να καθορίσει σημαντικά την ανάπτυξη των σύγχρονων αλγορίθμων ανίχνευσης από τους παθητικούς αισθητήρες μικροκυμάτων. Τέλος, η εφαρμογή της προτεινόμενης μεθοδολογίας σε ψευδο-επιχειρησιακή βάση κατά τη διάρκεια ενός ιστορικού πλυμμηρικού φαινομένου, παρέχει μια εκτίμηση της επιχειρησιακής εφαρμοσιμότητας και του συγκριτικού οφέλους που μπορεί να προκύψει από την υιοθέτηση της συγκεκριμένης μεθόδου σε συστήματα έγκαιρης πρόγνωσης και πρόληψης πλημμυρών.

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

2016 ◽  
Vol 8 (1) ◽  
pp. 22-31 ◽  
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

scholarly journals Error Analysis of Satellite Precipitation Products in Mountainous Basins

2014 ◽  
Vol 15 (5) ◽  
pp. 1778-1793 ◽  
Author(s):  
Yiwen Mei ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos ◽  
Marco Borga
Keyword(s):  
Error Analysis ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Regimes ◽  
Precipitation Estimation ◽  
High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

scholarly journals Evaluation of Satellite Precipitation Estimates over Australia

2020 ◽  
Vol 12 (4) ◽  
pp. 678 ◽  
Author(s):  
Zhi-Weng Chua ◽  
Yuriy Kuleshov ◽  
Andrew Watkins
Keyword(s):  
Rainfall Intensity ◽  
Austral Summer ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Notable Increase ◽  
Climate Prediction Center

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.

scholarly journals Precipitation Estimates from Commercial Microwave Links: Practical Approaches to Wet-antenna Correction

2021 ◽  
Author(s):  
Jaroslav Pastorek ◽  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Intensity ◽  
Rain Gauge ◽  
Model Parameters ◽  
Rainfall Events ◽  
Precipitation Estimates ◽  
Attenuation Models ◽  
Using Data ◽  
Quantitative Precipitation Estimates ◽  
Rain Gauge Network ◽  
Commercial Microwave Links

An inadequate correction for wet antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This paper analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and RMSE of 1 mm/h in the median could be retrieved, even from sub-kilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

scholarly journals The Development of a Two-Step Merging and Downscaling Method for Satellite Precipitation Products

2020 ◽  
Vol 12 (3) ◽  
pp. 398 ◽  
Author(s):  
Lu ◽  
Tang ◽  
Wang ◽  
Liu ◽  
Wei ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spline Interpolation ◽  
Auxiliary Variable ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Data Merging ◽  
Coarse Spatial Resolution ◽  
Precipitation Estimates

Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the accuracy of satellite precipitation estimates. The first step is data merging based on optimum interpolation (OI), and the second step is downscaling based on geographically weighted regression (GWR); therefore, the framework is called OI-GWR. An Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used to demonstrate the effectiveness of OI-GWR in the Tianshan Mountains, China. First, the original IMERG precipitation data (OIMERG) are merged with rain gauge data using the OI method to produce corrected IMERG precipitation data (CIMERG). Then, using CIMERG as the first guess and the normalized difference vegetation index (NDVI) as the auxiliary variable, GWR is utilized for spatial downscaling. The two-step OI-GWR method is compared with several traditional methods, including GWR downscaling (Ori_GWR) and spline interpolation. The cross-validation results show that (1) the OI method noticeably improves the accuracy of OIMERG, and (2) the 1-km downscaled data obtained using OI-GWR are much better than those obtained from Ori_GWR, spline interpolation, and OIMERG. The proposed OI-GWR method can contribute to the development of high-resolution and high-accuracy regional precipitation datasets. However, it should be noted that the method proposed in this study cannot be applied in regions without any meteorological stations. In addition, further efforts will be needed to achieve daily- or hourly-scale downscaling of precipitation.

Evaluation of High-Resolution Satellite Precipitation Products over Very Complex Terrain in Ethiopia

2010 ◽  
Vol 49 (5) ◽  
pp. 1044-1051 ◽  
Author(s):  
Feyera A. Hirpa ◽  
Mekonnen Gebremichael ◽  
Thomas Hopson
Keyword(s):  
Neural Networks ◽  
Complex Terrain ◽  
Tropical Rainfall Measuring Mission ◽  
High Elevation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Analysis ◽  
Climate Prediction Center ◽  
Microwave Data

Abstract This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

Sign in / Sign up

Export Citation Format

Share Document