Evaluation of Small-Satellite Architectures to Address the Future Needs of the NOAA Enterprise and its Stakeholders
The goal of this research is to explore quick and agile methodologies to entrain small-satellites that have limited lifetimes into the NOAA processing stream. The goal is to develop workflows that would allow NOAA, once it has identified an upcoming mission, to work with partners to ingest, calibrate, validate, and exploit these data in a minimum amount of time. The objective is twofold in that it would allow NOAA to better exploit upcoming constellations of very small satellites that have a very limited lifetime, as well as help NOAA assess the full utility of some small constellations of satellites being considered by the NOAA Satellite Observing System Architecture (NSOSA) study team.
NOAA’s operational HWRF excludes assimilation of cloudy and precipitation affected satellite radiances. Instead, clear-sky radiances are assimilated. Since clouds are part of the dynamics of hurricane processes, subsequent neglect of clouds during a data assimilation process may have adverse consequences to the prediction of hurricane track and intensity. One main goal of the proposed research aims to improve hurricane forecasts by creating an improved initial state via enabling cloud condensate cycling in the HWRF system in order to facilitate the assimilation of all-sky satellite radiances.
Establishing Links between Atmospheric Dynamics and Non-Gaussian Distributions and Quantifying Their Effects on Numerical Weather Prediction
This project is a continuation of a previous NSF-supported research project, “Analyzing the Impacts of Non-Gaussian Errors in Gaussian Data Assimilation Systems.” The goal of this project is to address several different aspects about how non-Gaussian distributed errors affect data assimilation and retrieval systems.
This project takes a multi-faceted, integrated approach to understanding and addressing significant forecasting challenges of atmospheric aerosol in the coastal zone impacting the characteristics of electro/optical (E/O) propagation.
In this research we plan to exploit the unique capabilities of the Geostationary Lightning Mapper (GLM) instrument through data assimilation with the NOAA Hurricane WRF operational system. We propose to add the GLM assimilation capability to the HWRF data assimilation system, which is based on the hybrid Gridpoint Statistical Interpolation (GSI) algorithm. The new HWRF/GSI system with the GLM assimilation capability will be evaluated in detail. Given high spatiotemporal coverage of GLM over open oceans, this research represents a great opportunity for potential improvement of the NOAA operational HWRF forecasts, in particular the prediction of rapid intensification of tropical cyclones.
(COMPLETED) R2O Transition of the GOES-R GLM Assimilation Capability in GSI for Use in the NCEP GDAS
Project Period: May 1, 2017 to April 30, 2018 Principal Investigator(s): Karina Apodaca Co-Principal Investigator(s): Milija Zupanski Sponsor(s): Developmental Testbed Center (DTC) Goal: Preparing the GOES-R GLM lightning assimilation development in the Gridpoint Statistical Interpolation system for using satellite lightning measurements in NOAA/NWS/NCEP Global Forecasting System. Final Report
(COMPLETED) Advancing coupled land-atmosphere modeling with NASA-Unified WRF via process studies and satellite-scale data assimilation
This project builds on the successful development and application of the NASA Unified Weather Research and Forecasting (NU-WRF) modeling system, with the goal of integrating and enhancing existing land and atmospheric data assimilation capabilities to advance regional-scale coupled land-atmosphere modeling for process studies.
(COMPLETED) Evaluation of the Newly Developed Observation Operators for Assimilating Satellite Cloud and Precipitation Observations in GSI within the HWRF system
In this project, we plan to perform a detailed evaluation of the impact of the new observation operators by employing an extensive set of selected cases and assessing their value in a systematic manner before they are ready for general/public use.
NCEP’s plans for regional data assimilation of the hurricane vortex and surrounding environment are to augment the current 3-D Var system with Situation-Dependent Background Errors (SDBEs).This research problem can be approached by investigating the balances determined by ensemble-based data assimilation, specifically the current class of Ensemble Kalman Filter (EnKF) schemes. We will employ an EnKF algorithm developed at Colorado State University, named the Maximum Likelihood Ensemble Filter (MLEF), for improving the formulation of balance constraints in SBDEs.
This NSF funded project investigated the impacts of non-Gaussian errors associated with a temperature and humidity 1-dimensional variational retrieval system called the CIRA 1-Dimensional Optimal Estimator, or C1DOE for short. As well as testing a mixed lognormal –Gaussian distribution formulation of a 1DVAR cost function against the standard Gaussian fits all and the logarithmic transform formulation, the PI and Co-PI developed the needed theory to extend the mixed distribution approach from the full field 3DVAR and 4DVAR formulation to incremental versions through the introduction of a geometric tangent linear approach.