scholarly journals Modified mean field bias and local bias for improvement bias corrected satellite rainfall estimates

MAUSAM ◽  
2021 ◽  
Vol 69 (4) ◽  
pp. 543-552
Author(s):  
GIARNO . ◽  
MUHAMMAD PROMONO HADI ◽  
SLAMET SUPRAYOGI ◽  
SIGIT HERUMURTI
Keyword(s):  
Mean Field ◽  
Local Bias ◽  
Field Bias ◽  
Satellite Rainfall ◽  
Rainfall Estimates

scholarly journals Bias correction of radar and satellite rainfall estimates and increasing its accuracy using modified merging

MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 377-390
Author(s):  
GIARNO GIARNO ◽  
HADI MUHAMMAD PROMONO ◽  
SUPRAYOGI SLAMET ◽  
HERUMURTI SIGIT
Keyword(s):  
Remote Sensing ◽  
Bias Correction ◽  
Mean Field ◽  
Tropical Rainfall Measuring Mission ◽  
Absolute Error ◽  
Data Types ◽  
Local Bias ◽  
Field Bias ◽  
The Stability ◽  
Rainfall Estimates

Bias correction in the weather radar and the tropical rainfall measuring mission (TRMM) rainfall estimates are used to improve its accuracy. This correction is usually done separately for both radar and TRMM. Even though the corrections are done separately, the results of these corrections can be further improved using the merging. Among the methods of merging, modified local bias, mean field bias and conditional merging may be suitable methods used to correct rainfall estimates from remote sensing surrounding in the Makassar Strait. The aim of this research corrects radar and TRMM rainfall estimates, then combining them to obtain more accurate rainfall estimates. The performance will be validated using correlation, root mean square error (RMSE) and mean absolute error (MAE). The result shows that modified mean field bias (Mod_MFB) and local bias (LB) can increase accuracy, mainly RMSE and MAE but not in correlation. However, conditional merging (CM) and modified LB can improve accuracy by increasing correlation and decrease RMSE and MAE. The modification of CM, LB modification and original estimation of remote sensing successively are the order of the best methods. Moreover, merging three data types is not automatically better than merging the two types of data. However, combination 3 types of data offer the stability of accuracy.

scholarly journals A susceptibility-based rainfall threshold approach for landslide occurrence

2019 ◽  
Vol 19 (4) ◽  
pp. 775-789 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin
Keyword(s):  
Landslide Susceptibility ◽  
Rainfall Threshold ◽  
Exceedance Probability ◽  
Equal Weight ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Continental Scale ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

scholarly journals Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia

2014 ◽  
Vol 15 (6) ◽  
pp. 2347-2369 ◽  
Author(s):  
Matthew P. Young ◽  
Charles J. R. Williams ◽  
J. Christine Chiu ◽  
Ross I. Maidment ◽  
Shu-Hua Chen
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Shallow Convection ◽  
Rain Gauges ◽  
The Mean ◽  
Satellite Rainfall ◽  
Convective Cells ◽  
Rainfall Estimates

Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

scholarly journals Evaluation of Satellite Rainfall Estimates over the Chinese Mainland

2014 ◽  
Vol 6 (11) ◽  
pp. 11649-11672 ◽  
Author(s):  
Yaxin Qin ◽  
Zhuoqi Chen ◽  
Yan Shen ◽  
Shupeng Zhang ◽  
Runhe Shi
Keyword(s):  
Chinese Mainland ◽  
The Chinese Mainland ◽  
Satellite Rainfall ◽  
Rainfall Estimates
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