Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system

Abstract

An adjoint‐based procedure for assessing the impact of observations on the short‐range forecast error in numerical weather prediction is described. The method is computationally inexpensive and allows observation impact to be partitioned for any set or subset of observations, by instrument type, observed variable, geographic region, vertical level or other category. The cost function is the difference between measures of 24‐h and 30‐h global forecast error in the Navy Operational Global Atmospheric Prediction System (NOGAPS) during June and December 2002. Observations are assimilated at 00utc in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS). The largest error reductions in the Northern Hemisphere are produced by rawinsondes, satellite wind data, and aircraft observations. In the Southern Hemisphere, the largest error reductions are produced by Advanced TIROS Operational Vertical Sounder (ATOVS) temperature retrievals, satellite wind data and rawinsondes. Approximately 60% (40%) of global observation impact is attributed to observations below (above) 500 hPa. A significant correlation is found between observation impact and cloud cover at the observation location. Currently, without consideration of moisture observations and moist processes in the forecast model adjoint, the observation impact procedure accounts for about 75% of the actual reduction in 24‐h forecast error.

Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system

“…Most NWP centers, which made effective early use of ATOVS (Advanced TIROS (Television and Infrared Observational Satellite) Operational Vertical Sounder), onboard NOAA (National Oceanic and Atmospheric Administration)-15/16/17/18/19, MetOp (The Meteorological Operational satellite A)-A/B and Aqua, have reported a substantial reduction in the forecast root mean square (RMS) error. Adjoint-based estimates of observation impact on NWP (Baker and Daley, 2000) have further demonstrated that the greatest decrease in forecast error is due to Advanced Microwave Sounding Unit-A (AMSU-A), which was launched as part of ATOVS and is used primarily for global atmospheric temperature sounding (Fourrié et al, 2002;Langland and Baker, 2004;Cardinali, 2009;Gelaro et al, 2010). Observations from Microwave Temperature Sounder-1 (MWTS-1) onboard and Fengyun-3B (FY-3B) have positive impacts on NWP forecasts (Lu et al, 2010;Lu and Bell, 2012;Zou, 2013, 2014;Li and Liu, 2015).…”

Predicting air quality: Improvements through advanced methods to integrate models and measurements

“…This is called the targeted observations technique. Different techniques for the placement of observations in the context of numerical weather prediction models are discussed in [14,90,77,57,88,83,10]. Studies performed during field experiments revealed the potential benefits that may be achieved using adaptive observations as well as various practical issues and shortcomings of the current targeting methodologies.…”

“…This may be achieved in a posteriori analysis and data assimilation experiments that are used to provide valuable insight on the benefits and shortcomings of various targeted observations strategies. Langland and Baker use forecasts of different lengths that verify at the same time to define J v and to assess the observation impact on the forecast error [77]. For a priori experimental planning the forecast error at t v is not known when selecting targets (planning stage), nor are the values of future observational data to be assimilated.…”

Observing System Impacts on Estimates of California Current Transport

“…The contribution of each observation y o i to any scalar function I(x) of the state vector x is of particular interest, where I(x) represents any metric of interest such as ocean transport, eddy kinetic energy, eddy fluxes, etc. Following Langland and Baker, 9 it is easy to show that the change in I(x) due to assimilating the observations is given by the first-order Taylor expansion:…”