Optimally merging precipitation to minimize land surface modeling errors

Yılmaz, Mustafa Tuğrul
Shrestha, Roshan
Anantharaj, Valentine G.
This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation.
Journal of Applied Meteorology and Climatology


Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios
OKKAN, UMUT; İnan, Gül (Wiley, 2015-09-01)
In this study, statistical downscaling of general circulation model (GCM) simulations to monthly inflows of Kemer Dam in Turkey under A1B, A2, and B1 emission scenarios has been performed using machine learning methods, multi-model ensemble and bias correction approaches. Principal component analysis (PCA) has been used to reduce the dimension of potential predictors of National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. Then, the reasonabl...
Improving land data assimilation performance with a water budget constraint
Yılmaz, Mustafa Tuğrul; Houser, Paul R. (American Meteorological Society, 2011-10-01)
A weak constraint is introduced in ensemble Kalman filters to reduce the water budget imbalance that occurs in land data assimilation. Two versions of the weakly constrained filter, called the weakly constrained ensemble Kalman filter (WCEnKF) and the weakly constrained ensemble transform Kalman filter (WCETKF), are proposed. The strength of the weak constraint is adaptive in the sense that it depends on the statistical characteristics of the forecast ensemble. The resulting filters are applied to assimilat...
Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting
Yılmaz, Koray Kamil; Hsu, KL; Sorooshian, S; Gupta, HV; Wagener, T (American Meteorological Society, 2005-08-01)
This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agre...
Predictability of Seasonal Precipitation Using Joint Probabilities
Yılmaz, Mustafa Tuğrul (2010-01-17)
This paper tests whether seasonal mean precipitation is predictable using a new method that estimates and analyzes joint probabilities. The new estimation method is to partition the globe into boxes, pool all data within the box to estimate a single joint probability of precipitation for two consecutive seasons, and then apply the resulting joint probability to individual pixels in the box. Pooling data in this way allows joint probabilities to be estimated in relatively small sample sizes, but assumes that...
Extreme value analysis and forecasting of maximum precipitation amounts in the western Black Sea subregion of Turkey
Yozgatlıgil, Ceylan (Wiley, 2018-12-01)
Monthly maximum precipitation amounts for the period 1950-2010 were modelled for seven climatological stations in the western Black Sea subregion of Turkey using a distributional and time series analysis approach. First, the generalized extreme value (GEV) distribution was fitted using the location parameter of the GEV distribution as a function of several explanatory variables that affect the maximum precipitation. We quantified the change in extreme precipitation for each location and derived estimates of...
Citation Formats
M. T. Yılmaz, R. Shrestha, and V. G. Anantharaj, “Optimally merging precipitation to minimize land surface modeling errors,” Journal of Applied Meteorology and Climatology, pp. 415–423, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34437.