Initialization of Numerical Forecast Models with Satellite data Meteorology 415 Fall, 2010

Atmospheric Numerical Models Basic Limits Initial Conditions (resolution) Boundary Conditions (forces) Physics (inexact, empirical relationships) Round-off errors (computational) Chaos (systemic)

Models Limits

Atmospheric Numerical Models Starting the Model Start with first guess field Usually a 6 hour forecast from same model Advantages: On same grid domain with the parameters needed Reasonable assumption – errors accumulate with time Computational short-cuts Adjust with Observations - window of opportunity - discern good v bad reports - automate the process

Most Common Data Sources RUC = Rapid Update Cycle – a forecast model run every 3 hrs with projections to 12hrs

The WRF Initialization

Reminder on WRF Initialization

Other Data Sources

SST Effects from Satellite

Snow/Ice Effects from Satellite

Terrain Definition in GSI

Better Resolution in Vertical

Non-Hydrostatic Effects in Mts

Global Model Initial Conditions

Satellite Input – Quality Assurance Water Vapor derived winds: 300-700mb [1847 accepted] IR derived winds: 700-1000mb [7048 accepted] IR derived winds: 150-300mb [4653 accepted]

Estimating Model Radiance

Shortcomings of Model Estimates Radiative transfer law approximations are applied. Radiances from several different satellite channels are used together to produce one temperature sounding. The derived soundings essentially are layer averages in layers defined by the absorber weighting functions for the observed radiation wavelengths. These are interpolated to much thinner model layers to compare against model fields, or they are interpolated to standard sounding levels and model data are also interpolated to standard sounding levels for comparison.

Shortcomings of Model Estimates Errors in various packages Analysis Schemes Observational (instrument) Representativeness* Model physics *Example: Satellite microwave soundings (actually, radiances) over the ocean. These are the only source of temperature profiles in cloudy regions! Resolving only 3 or 4 thick tropospheric levels, they vertically smear out model-resolved tropopause folds and sloping frontal zones. If use of this data degrades the background fields, then the data should be rejected.

Data Reliability Ascertaining what to keep and throw away

Data Reliability Ascertaining what to keep and throw away – The No Surprise Snowstorm – Jan 25, 2000

Data Reliability Known error distributions for GOES in 4DAS

Challenges of Using Satellite Data Any radiation that's sensed comes from a deep layer of the atmosphere, so vertical resolution is coarser than model vertical resolution This will improve greatly when interferometers replace radiometers. This is not scheduled on GOES until at least GOES-S The proper conversion of satellite radiances to temperatures requires knowing the emissivity at the bottom of the layer being sensed. This presents problems over land, so data over land are only reliable for channels sensing the upper troposphere and stratosphere

Atmospheric Numerical Models The Pitfalls of Data Assimilation SUMMARY Data Void regions (particularly the oceans) Bad First Guess Fields Good Data rejected Analysis Assumptions

Fixing Errant Data with Complex Quality Control References Fixing Errant Data with Complex Quality Control Collins, W.G., 1997: The use of complex quality control for the detection and correction of rough errors in rawinsonde heights and temperatures: A new algorithm at NCEP/EMC. NCEP Office Note 419, 49 pp. Julian, P.R., 1989: Quality control of the aircraft file at the NMC. Part I. NCEP Office Note 358, 13 pp. [Note - NCEP office notes are scheduled to be available online within a few months of publication of this module] References on Many Aspects of How 3D-VAR Works in the Global Data Assimilation System Derber, J. C., D.F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Wea. and Forecasting, 6, 538-547.

References Derber, J. C. and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287-2299. McNally, A.P., J.C. Derber, W.-S. Wu, and B.B. Katz, 2000: The use of TOVS level-1B radiances in the NCEP SSI analysis system. Quart. J. Roy. Meteor. Soc., 126, 689-724. Parrish, D. F. and J. C. Derber, 1992: The National Meteorological Center's spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763. Spatial Patterns of Model Error Used in 3D-VAR Analysis Derber, J. C. and F. Bouttier, 1999: A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus, 51A, 195-221