Which merging technique is supposed to be best for combining gridded satellite and location-specific gauge precipitation estimates?
The CMAP algorithm by Xie & Arkin was used on monthly scale. Later they have used it on daily scale. It is a good one as they define the error structure of different obs type from past data, and this information is used while deciding weight in the Optimum Interpolation method. But now the issue is on how to include Radar rainfall in this algorithm ? This has not been demonstrated.
Hi.
There are several approaches in the literature, especially for hydrological applications. However, the most recent method trying to put together precipitation data from various sources is MSWEP:
Article MSWEP: 3-hourly 0.25° global gridded precipitation (1979–201...
However, the answer is very complex and depends on the scale, terrain and, last but not least, the application. Of course, intercalibration of the various sources is a prominent issue. A recent assessment was published using gauge data and hydrological modeling:
Article Global-scale evaluation of 22 precipitation datasets using g...
Best,
Vincenzo
The agreement with rain gauges and other satellite derived estimates in space and time introduces uncertainties linked to the sparse sampling and also due to the different type of measurements made by ground-based instruments such as rain gauges and by space-borne observations . It should always be taken into account that temporal range of the space-borne sensor measurements is not really “instantaneous”, and it refers to the cloud volume where the rainfall originates. The relationship between the measurement and the surface precipitation is highly dependent on the type of cloud. The rain gauges, on the other hand, measure directly the precipitation near the surface, and the result is based on integration over time. Similarly, satellite rainfall products may not able to detect the rainfall amount of the warm clouds as the cloud-tops would be too warm for IR thresholds, and there will not be much ice aloft to be detected by these sensors. However, sensors could detect the rainfall from the deep convection. Thus, the satellite products may detect only part of the rainfall. This may partly explain the underestimation by most of the satellite products over India.
Please see..
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.442.9623&rep=rep1&type=pdf
Please look into here too..
http://onlinelibrary.wiley.com/doi/10.1017/S1350482700001468/pdf
To be checked and compare..
https://hal.archives-ouvertes.fr/hal-00304593/document
Recently, we used the quantile regression forests (QRF) tree-based machine learning regression model (Meinshausen, 2006) to combine dynamic and static land surface variables together with multiple global precipitation sources to stochastically generate improved precipitation ensembles(combined product) for the study area of Iberian Peninsula.
https://www.hydrol-earth-syst-sci.net/22/1371/2018/hess-22-1371-2018.pdf
Available combined product : https://sites.google.com/uconn.edu/ehsanbhuiyan/research?authuser=0
Hi If you use Precipitation from remote sensing products, you can improve their performance using local raingauges. The merging algorithm described in this work have proved to be successful in reducing the remote sensed field error. In the paper the parameters of the merging are analyzed against the raingauges density so that one defines their value with the features of the local raingauge network and, therefore, one will not need to perform a calibration procedure.
Conference Paper Analysis of the kernel bandwidth influence in the double smo...
The Water Cycle Integrator of the eartH2Observe project have several global precipitation dataset at
http://www.earth2observe.eu/
Regards
You might try the recently published RF-MEP methodology:
RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements. doi: 10.1016/j.rse.2019.111606
https://authors.elsevier.com/c/1aKrd7qzSnJWL
Hi, You can improve its accuracy by merging information. Want to improve the performance of your hydrological model on a scarce data basin?!! We introduce you to our new article where a satellite-reanalysis-gauge merging algorithm is detailed. Article Improving Rainfall Fields in Data-Scarce Basins: Influence o...
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