Statistics for Spatial Data.

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events. The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) ...

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The Global Precipitation Measurement Mission

[1] This first paper of the two‐part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah‐MP). The Noah‐MP’s performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long‐term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture‐groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local‐scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah‐MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah‐MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah‐MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah‐MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2010JD015139

The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements

Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall

Correction to “Stochastic interpolation of rainfall data from rain gages and radar using cokriging: 1, Design of experiments” by D.‐J. Seo, W. F. Krajewski, and D. S. Bowles

Using Wavelet Analysis to Assess Timing Errors in Streamflow Predictions

We describe adaptive conditional bias (CB)-penalized ensemble Kalman filter (AEnKF) to improve estimation and prediction of extremes. Environmental variables are generally observed with significant uncertainties. Geoscientific data assimilation (DA) is hence often subject to CB which adversely impacts estimation of extremes. A generalization of EnKF, AEnKF accounts for CB by dynamically reflecting the flow-dependent information content in the model prediction relative to that in the observation via a scaler weight. The implicit dependence of the weight on the posterior state renders the AEnKF solution nonlinear for superior performance over the tails of the predictand as well as in the (unconditional) mean sense. AEnKF prescribes the weight in real time by minimizing the degrees of freedom for noise. Real-time optimization of the weight also means that AEnKF obviates or reduces the need for calibration of uncertainty parameters which is often subjective and expensive. Comparative evaluation for flood prediction shows that AEnKF outperforms EnKF by a very significant margin but is about two to three times more expensive computationally. Because AEnKF uses the EnKF solution, it can be easily implemented in any EnKF code with the addition of the weight optimization module. Given superior performance and relative ease of implementation, AEnKF should be favored over KF in a wide range of geoscientific DA, particularly when performance over tails is important. 

Adaptive conditional bias-penalized Kalman filter with minimization of degrees of freedom for noise for superior state estimation and prediction of extremes

iSPUW: integrated sensing and prediction of urban water for sustainable cities

Radar and multisensor quantitative precipitation estimates (QPEs) have played a critical role in real-time hydrologic and weather predictions. The utility of this data set for hydrologic model calibration, however, is hampered by the limited data archive and the presence of data gaps. In the research reported in this paper, the use of a composite reflectivity data set is investigated, namely the Weather Services International (WSI) National Operational Weather radar (NOWrad) data set, to create an hourly QPE that would complement the National Weather Service (NWS) Stage III archive by filling in the latter’s gaps and potentially improving the latter’s accuracy. The paper starts from an inventory analysis of Stage III and NOWrad products, and through which it is found that as much as 17% of the Stage III data were missing for some of the regions, and some of these gaps can be filled using the NOWrad data. Then, the paper presents two experimental products, as follows: (1) NOWrad–Raw, based on varia...

Dong Jun Seo

Papers

Correction to “Stochastic interpolation of rainfall data from rain gages and radar using cokriging: 1, Design of experiments” by D.‐J. Seo, W. F. Krajewski, and D. S. Bowles

Using Wavelet Analysis to Assess Timing Errors in Streamflow Predictions

Adaptive conditional bias-penalized Kalman filter with minimization of degrees of freedom for noise for superior state estimation and prediction of extremes

iSPUW: integrated sensing and prediction of urban water for sustainable cities

Creation of multisensor precipitation products from WSI NOWrad reflectivity data