Estimating Observation Impact with the NAVDAS Adjoint System

Transcription

1 Estimating Observation Impact with the NAVDAS Adjoint System Rolf Langland, Nancy Baker Marine Meteorology Division Naval Research Laboratory (NRL) Monterey, CA USA Talk presented by Ronald Errico 1

2 Terminology The analysis procedure: NAVDAS: NRL Atmospheric Variational Data Assimilation System - The forecast model: NOGAPS: Navy Operational Global Atmospheric Prediction System - 2

3 Data Assimilation Equation ANALYSIS BACKGROUND (6h) FORECAST [ ] ( ) + y x T T x x = PH HPH R H a b b b b 1 Temperature OBSERVATIONS Winds Pressure 3

4 Adjoint of Assimilation Equation Sensitivity to Observations: J y = [ T + ] HP H R HP b 1 b J x a Adjoint of forecast model produces sensitivity to x a Sensitivity to Background: J J T J = H x x y b a 4

5 Observation Impact Question Can we use observation sensitivity to estimate, with good accuracy, the impact of any or all observations assimilated by NAVDAS on a measure of short-range forecast error? 5

6 Observation Impact Equation ( y Hx ), 30 δ e = = 24 OBSERVATIONS J estimate of e e b y BACKGROUND (6h) FORECAST SENSITIVITY GRADIENT FROM NAVDAS ADJOINT Each observation assimilated by NAVDAS contributes to a reduction or increase in e 24 -e 30 *derived in Langland and Baker (Tellus, in-press 2004) 6

7 Impact of Observations on Forecast Error OBSERVATIONS ASSIMILATED AT 00UTC IN NAVDAS e 30 X b X a +24h e 24 e is a quadratic energy-weighted global forecast error The forecast error difference e 24 e 30 is due entirely to the assimilation of observations at 00UTC 7

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9 Observation Impact Procedure 1. Define forecast error 2. Run adjoint of forecast model 3. Run adjoint of assimilation procedure dj/dy 4. Compute observation impact (no moisture data at present) e x 24, a e x 30 b Computational Cost: Less than regular assimilation and forecast model (uses innovations already produced by regular run of NAVDAS). 8

10 Sensitivity of J= 24h global forecast error Sensitivity to analyzed initial conditions from adjoint of forecast model 00UTC 10 December

11 Observation Impact on e 24 -e 30 Rawinsonde Profiles Decrease Increase Σ = J kg Assimilation Time: 00UTC 10 Dec 2002

12 Critical Rawinsonde Locations 20 Raobs that produce largest reduction in e e December

13 Observation Impact on e 24 -e 30 Commercial Aircraft Observations Decrease Increase Σ = J kg -1 Assimilation Time: 00UTC 10 Dec

14 Observation Impact on e 24 -e 30 ATOVS Temperature Retrievals Decrease Increase Σ = J kg -1 Assimilation Time: 00UTC 10 Dec

15 Observation Impact on e 24 -e 30 Geosat Wind Observations Decrease Increase Σ = J kg -1 Assimilation Time: 00UTC 10 Dec

16 Observation Impact by hemisphere Ob count40 3,000,000 2,000, ,000,000 0 SHEM NHEM Number of data assimilated at 00UTC J kg ATOVS SATW AIRW LAND SHIP AUSN Impact of data on e 24 -e RAOB June and December

17 Observation Impact vertical 30distribution e e Includes all satellite and in-situ observations assimilated at 00UTC December 2002 Pres (hpa) J kg -1 Line length is proportional to number of obs in layer 16

18 Observation Impact Estimate J kg δe 24 estimate using dry adjoint procedure Temperature, wind and height observations explain 75 % e 24 -e 30 in nonlinear model forecasts Moisture obs account for remaining 25 % December

19 Applications of NAVDAS Adjoint System Targeted Observing Guidance Observing Network Design (Real or Hypothetical) Tuning Assimilation Parameters Observation Error Variance Quality Control (Observation monitoring and Identification of bad obs) End of Talk 18

20 Reduction in Forecast Error due to Observations assimilated at 00UTC December 2002 e 30 (from X b ) e 24 (from X a ) The error in the forecast starting from X a is reduced because observations have been assimilated to produce a more accurate trajectory Low Forecast Error (J kg -1 ) High

21 Sensitivity of 24h global forecast error to Initial Conditions December 2002 Low High

22 Forecast and Analysis Procedure Observation (y) Background (x b ) Data Assimilation System Analysis (x a ) Forecast Model Forecast (x f ) Adjoint of Forecast and Analysis Procedure Observation Sensitivity ( J/ y) Background Sensitivity ( J/ x b ) Adjoint of the Data Assimilation System Analysis Sensitivity ( J/ x a ) Adjoint of the Forecast Model Tangent Propagator Gradient of Cost Function J: ( J/ x f ) Observation Impact <y-h(x b )> ( J/ y) What is the impact of the observations on measures of forecast error (J)?

23 Scatter plot result ATOVS ATOVS Forecast Error Increase (J kg -1 ) Forecast Error Decrease LATITUDE

24 Large Impact of Observations in Cloudy Regions Ob Impact SATWIND RAOB ATOVS Cloud Cover (% ) Fig. 4: Observation impact (average magnitude per observation, in J kg -1 ) as a function of model-diagnosed cloud-cover. The impact in this figure includes both improvements and degradations of 72h global forecast error. Based on results from 29 June 28 July 2002.

25 Also include gradient test equation and time series J obs vs J grid (within 5%). Caveats: 17

26 Conclusions -