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What is a Reanalysis?

Wind Resource Modeling & Uncertainty Quantification for Wind Resource Assessment: Application to Bangladesh. What is a Reanalysis?. Task 1: Review of the global reanalysis data sets available for WRF initialization. What is a Reanalysis?.

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What is a Reanalysis?

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  1. Wind Resource Modeling & Uncertainty Quantification for Wind Resource Assessment: Application to Bangladesh

  2. What is a Reanalysis? Task 1: Review of the global reanalysis data sets available for WRF initialization

  3. What is a Reanalysis? • Reanalysis: (Retrospective Analysis), is a scientific method for developing a comprehensive record of how weather and climate are changing over time. • A synthesized estimate of the state of the atmosphere, ocean and land by objectively merging observationsand a numerical model information. • A reanalysis typically extends over several decades or longer, and covers the entire globe or a region from the Earth’s surface to well above the stratosphere.

  4. What is a Reanalysis? Dynamical consistency Historical Observations Model Data Assimilation Truth Long records of gridded data Reanalysis Data

  5. Latest Atmospheric Reanalyses • The main characteristics of the third generation reanalyses are: • Higher resolution (< 1°) • Fully coupled atmosphere-ocean models • Advanced data assimilation (3D & 4D-VAR) • Assimilation of satellite radiances • Third generation reanalysis data sets currently available: • ECMWF European Re-Analysis Interim (ERA-Interim) • NASA Modern Era Reanalysis for Research and Applications (MERRA) • NCEP Climate Forecast System Reanalysis (CFSR) • Japan Meteorological Agency JRA-55: (underway and not reviewed)

  6. The satellite era:1979-present log(data count) satellites upper-air surface 1900 1938 1957 1979 2012 Number of assimilated observations in reanalyses

  7. Time series of observations type and platforms assimilated in ERA-Interim

  8. Reanalysis Data Assimilation Challenges • Changes in the observing system strongly affect trends in many fields; for example, SSM/I in 1987 and the change from TOVS to ATOVS instrument on NOAA satellites in 1998 and 2001 • Spatial discontinuity in central African moisture fields associated with the type of rawinsonde instruments separately used by countries

  9. Reanalysis Data Assimilation Challenges ERA40 2nd generation ERA-I 3rd generation Observations SSM/I assimilation Impact of SSM/I assimilation on global precipitation in ERA reanalysis

  10. Reanalysis Data Assimilation Challenges NOAA 16 --> • TOVS > ATOVS • AMSU-A • NOAA-15 Oct 1998 • NOAA-16 Feb 2001 NOAA 15 --> CFSR mean precipitable water analysis increment

  11. Downscaling • Global reanalyses provide the initial conditions and the lateral boundary forcing to the Weather Research and Forecasting (WRF) model. • The following fields are needed by WRF: • Upper-air: pressure, geopotential height, temperature, humidity, wind components • Surface: terrain height, sea level pressure, surface pressure, 2m temperature, 2m humidity, 10m wind components, skin temperature • Soil: temperature, water content, skin temperature • Sea surface temperature

  12. Downscaling • Not all the fields needed by WRF are offered at the full global reanalysis resolution. • For example the CFSR model is run at 0.3° but upper-air products made available to the community are available at 0.5° only . • Not all the fields needed by WRF are “analyzed”. • For example the CFSR 2-meter temperature is forecasted only . In the following, we give the spatial resolution and temporal frequency of the reanalysis products effectively available to initialize WRF. They can differ from the model resolution and output frequency that was used to generate the reanalysis.

  13. 3rd Generation analyses

  14. ECMWF ERA-Interim • Features: • Purpose: ERA-Interim was planned to replace ERA-40. • Model: December 2006 version of the ECMWF Integrated Forecast Model (IFS Cy31r2) • Data assimilation: a 12-hourly four-dimensional variational analysis (4D-Var) with adaptive estimation of biases in satellite radiance data • Period: January 1 1979 – present • Resolution: 0.75°x 0.75° x 37 levels, upto 0.1 hPa, 6-hourly • Format: GRIB2

  15. ECMWF ERA-interim • Key Strengths • Spatially and temporally complete data set of multiple variables at high spatial and temporal resolution • Improved low-frequency variability (compared to ERA-40) • Improved stratospheric circulation (compared to ERA-40) • Key Weaknesses • Too intense of a water cycling (precipitation, evaporation) over the oceans • In the Arctic: positive biases in temperature and humidity below 850hPA compared to radiosondes; does not capture low-level inversions

  16. ECMWF ERA-Interim Reanalysis Latency: 3 months Completeness: ERA interim reanalysis has the full set of fields needed by WRF Data Access: ECMWF (free but registration required): http://apps.ecmwf.int/datasets/data/interim_full_daily/ NCAR: (US institutions only) http://rda.ucar.edu/datasets/ds627.0/

  17. NASA Modern Era Reanalysis for Research and Applications (MERRA) • Features: • Purpose: hydrological cycle on a broad range of weather and climate time scales • Model: 2009 version of the Goddard Earth Observing System Data Assimilation System Version 5 • Data assimilation: Incremental Analysis Updates (IAU) to slowly adjust the model states toward the observed statistics • Period: January 1 1979 – present • Data resolution: 0.5° (latitude) x 0.666° (longitude) x 42 levels (up to 0.01 hPa), hourly output (surface), 6-hourly output (upper-air) • Format: NETCDF

  18. NASA Modern Era Reanalysis for Research and Applications (MERRA) • Key Strengths • Significant improvement in precipitation and water vapor climatology • higher frequency output including selected hourly fields • Provides vertical integrals and analysis increment fields for the closure of atmospheric budgets • Key Weaknesses • Assimilation routine is “frozen” and will not be updated for newer satellite instruments, so quality will eventually degrade as current instruments expire

  19. NASA Modern Era Reanalysis for Research and Applications (MERRA) Latency: 3 weeks Completeness: MERRA has no soil moisture fields that required to initiate mesoscale models. Thus soil fields have to be found from other data sources like NASA GLDAS. In addition, MERRA has no pressure data over high terrain, which requires a special treatment before the data can be applied as a driver to mesoscale model for downscaling. Data Access: NASA : http://disc.sci.gsfc.nasa.gov/mdisc/

  20. NCEP Climate Forecast System Reanalysis (CFSR) • Features • Purpose: Advancing climate studies by eliminating fictitious trends caused by model and data assimilation changes in real time. • Model: 2007 NCEP Global Forecast System T382L64 (~38km) • Data Assimilation: NCEP Global Statistical Interpolation (3D-VAR) • Period: January 1, 1979 – present. • Data resolution: Surface: 0.3° x 0.3°, 6-hourly; Upper-air: 0.5° x 0.5°, 37 levels, up to 0.1hPa, 6-hourly. • Format: GRIB2

  21. NCEP Climate Forecast System Reanalysis (CFSR) • Key Strengths • Accounts for changing CO2 and other trace gasses, aerosols, and solar variations • Highest horizontal resolution • Key Weaknesses: • Cold bias • Noisy tropical cyclones • Soil moisture • Fit to observations in the equatorial zone

  22. NCEP Climate Forecast System Reanalysis (CFSR) Latency: 1 day Completeness: CFSR has the full set of fields needed by WRF Data Access: NOAA: http://cfs.ncep.noaa.gov/ NCAR: http://rda.ucar.edu/datasets/ds093.0/

  23. Recommendations • Each reanalysis has its specific strengths and weaknesses, but there is no product that outperforms the others. • We recommend CFSR for the following reasons: • It has the highest horizontal resolution. • Reanalyses are updated daily. • The data are available from NCAR RDA • It is unified with NCEP operational forecast. • In the future a transition from reanalysis to forecast will be seamless. • It is unified with NCEP’s Climate Forecast System. • CFS provides 4-times a day 9 month forecasts that could potentially be used for seasonal forecast of wind resources.

  24. CFSR versus 1st and 2nd generation reanalysis Observations NCEP/NCAR 2nd generation NCEP/NCAR 1st generation CFSR 3rd generation 24 Rainfall

  25. MJO Intraseasonal Rainfall CFSR versus 1st and 2nd generation analysis NCEP/NCAR 2nd generation Observations CFSR 3rd generation NCEP/NCAR 1st generation 25

  26. Tropical Instability Waves (TIW) CFSR validation observations SST (Shading) ; 10-m Wind (Contour) 26

  27. WRF 3-domain configuration used to downscale ERA-I, MERRA & CFSR data between Feb. 11 and Feb. 20, 2014Δx = 27km/9km/3km Example of WRF downscaling 27

  28. WRF downscaled 50m-wind from ERA-I, MERRA and CFSR valid Feb. 20, 2014 at 07UTC (noon local time) Example of WRF downscaling Δx = 27km ERA-I MERRA CFSR 28

  29. WRF downscaled 80m-wind from ERA-I, MERRA and CFSR valid Feb. 20, 2014 at 07UTC (noon local time) Example of WRF downscaling Δx = 27km ERA-I MERRA CFSR 29

  30. WRF downscaled 100m-wind from ERA-I, MERRA and CFSR valid Feb. 20, 2014 at 07UTC (noon local time) Example of WRF downscaling Δx = 27km ERA-I MERRA CFSR 30

  31. WRF downscaled 50m-wind from ERA-I, MERRA and CFSR valid Feb. 20, 2014 at 07UTC (noon local time) Example of WRF downscaling Δx = 3km ERA-I MERRA CFSR 31

  32. WRF downscaled 80m-wind from ERA-I, MERRA and CFSR valid Feb. 20, 2014 at 07UTC (noon local time) Example of WRF downscaling Δx = 3km ERA-I MERRA CFSR 32

  33. WRF downscaled 100m-wind from ERA-I, MERRA and CFSR valid Feb. 20, 2014 at 07UTC (noon local time) Example of WRF downscaling Δx = 3km ERA-I MERRA CFSR 33

  34. Summary • The third generation of reanalyses: ERA-Interim, MERRA and CFSR can provide initial and lateral boundary conditions for the WRF model • The 3 reanalyses have a resolution between 0.3° and 0.75° and cover the period Jan. 1979 – present with 6-hourly outputs • Each reanalysis has its strength and weakness. There is no overall superior data set • We recommend CFSR reanalysis for its higher resolution and its integration with other NCEP products (real-time forecast and climate forecast) that could turn to be useful for the future of the project

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