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) ...

/pdf/the-global-precipitation-measurement-mission-4bkud4z4i5.pdf

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

When there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simulated outlet flow to the model states that are located more closely to the outlet in the hydraulic sense, and the subsequent overcompensation of the states in the more influential grid boxes to make up for the larger scale bias. In this work, we describe Mean Field Bias (MFB)-aware variational (VAR) assimilation, or MVAR, to address the above. MVAR performs bi-scale state updating of the distributed hydrologic model using streamflow observations in which MFB in the model states are first corrected at the catchment scale before the resulting states are adjusted at the grid box scale. We comparatively evaluate MVAR with conventional VAR based on streamflow assimilation into the distributed Sacramento Soil Moisture Accounting model for a headwater catchment. Compared to VAR, MVAR adjusts model states at remote cells by larger margins and reduces the Mean Squared Error of streamflow analysis by 2–8% at the outlet Tiff City, and by 1–10% at the interior location Lanagan.

Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models

Improving Medium-range Probabilistic Quantitative Precipitation Forecast for Heavy-to-extreme Events through the Conditional Bias-penalized Regression

The National Weather Service (NWS) runs an operational, multi-stage, precipitation processing system which determines rainfall accumulation estimates over a variety of spatial and temporal scales for use in forecasting, warning and numerical modeling applications and for dissemination to the general public. The primary input data to this system are radar reflectivity factor returns provided by the Weather Surveillance Radar-1988 Doppler (WSR-88D) radars of the Next Generation Weather Radar (NEXRAD) program. Since 1991 over 160 of these radars have been deployed, providing nearly contiguous coverage across most of the United States. Rain gage data are also incorporated at various stages of the system, principally to provide calibration of radar rainfall estimates. As the processing proceeds across NWS venues from a local to a regional to a national level, numerous quality control operations are performed, radar rainfall data are composited together spatially (mosaicked), and a wide variety of products are generated including alphanumeric, graphics-display, and high-resolution, digital-data. The products, which are updated as often as every five minutes, provide guidance to forecasters and input to hydrologic and other numerical models. In some instances, they are made available to users outside the Weather Service, as well.

Radar-Based Precipitation Processing for NWS Hydrologic Forecast Services

Recursive estimators of mean-areal and local bias in precipitation products that account for conditional bias

Accurate and timely prediction of flash flooding events can be a very useful tool for stormwater officials and first responders. Having lead time with which to issue evacuation directives, to close flood prone roadways, to deploy rescue gear and personnel, and to fortify areas against flooding is essential to minimize property damage and risk of casualties. In this poster, we are presenting a flash flooding prediction workflow based on the Hydrology Lab-Research Distributed Hydrologic Model (HL-RDHM). This workflow leverages cloud computing and the Pegasus Workflow Management System to provide continuous high resolution flood predictions for the Dallas-Fort Worth Metroplex area in North Texas, and can be easily expanded to other regions.

Dong Jun Seo

Papers

Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models

Improving Medium-range Probabilistic Quantitative Precipitation Forecast for Heavy-to-extreme Events through the Conditional Bias-penalized Regression

Radar-Based Precipitation Processing for NWS Hydrologic Forecast Services

Recursive estimators of mean-areal and local bias in precipitation products that account for conditional bias

Predicting Flash Floods in the Dallas-Fort Worth Metroplex Using Workflows and Cloud Computing