Update and TOA Radiation

Vacations in August have limited the posting, but the validation experiments are continuing. The experiments are moving well, and the system performance will be discussed in another post. Here are some of the validation comparisons for TOA radiation of some reanalyses to CERES ERBE-like data from Terra and Aqua. In the figures, the GEOS5 validation experiment is labeled d5_b10p15 (d is the 1/2 degree resolution, beta10 patch15 is the version of the data assimilation system).

The first figure is the zonal mean of the TOA LW and SW fluxes in Jan and Jul 2004. CERES observations are in red, GEOS5 is blue, JRA-25 is black and NCEP RII is green. GEOS5 shows somewhat smaller bias in the tropics LW, and also mid latitude SW. The July upward SW in the tropics seems biased high compared to the obs and other reanalyses, but otherwise, it seems in the range of the reanalyses. (Click on thumbnails to see the full figure)



The next two figures show the monthly mean maps of the comparisons. to CERES Terra. The differences between the CERES-Terra and CERES-Aqua are provided as a reference. In TOA LW, JRA-25 seems systematically biased high, while NCEP RII has strong positive and negative variations. GEOS5 leans to a high bias, but not as high as JRA. JRA and NCEP also show large positive bias in the tropics, which impling a dry upper troposphere or low cloud top. Newman et al (2000) have evaluated the interal consistency of several reanalyses, between OLR, precipitation and upper level divergence. In addition, they note that the correspondence among the reanalyses is quite low. The reanalyses OLR are all different from each other.



The shortwave biases of the reanalyses generally are similar. The exception seems to be the polar warm season. For example, TOA SW in Antarctica January is high for NCEP RII and GEOS5, but low for JRA.




Newman M., P. D. Sardeshmukh, J. W. Bergman, 2000: An Assessment of the NCEP, NASA, and ECMWF Reanalyses over the Tropical West Pacific Warm Pool. Bull. Amer. Meteror. Soc. 81, 41–48.

Smith, G. L.; Wielicki, B. A.; Barkstrom, B. R.; Lee, R. B.; Priestley, K. J.; Charlock, T. P.; Minnis, P.; Kratz, D. P.; Loeb, N.; 2004: Clouds and Earth Radiant Energy System (CERES): An overview, Advances In Space Research, 33, 1125-1131.

Wielicki, B.A., B.R. Barkstrom, E.F. Harrison, R.B. Lee, G. Louis Smith, and J.E. Cooper, 1996: Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment. Bull. Amer. Meteor. Soc., 77, 853–868.

Historical Satellite Assimilation

When evaluating one of our first analyses in the 1980s, we found some results very different from what was apparent in the recent (2001-2006) experiments. The experiment was run to test the impact of SSMI on the reanalysis time series (See this report). THe increments were drying the lower tropospehre continuously, and precipitation was small and getting smaller. Several aspect of the system were studied to pin down the problem. One hypothesis was that older coefficients (for the radiative transfer model) for the historical satellite data were causing some of the problems. NOAA NESDIS was in the process of creating new coefficients, and were able to make the new values available for testing, and now for the whole reanalysis period.

The figure below shows the time series of analysis increments of temperature and moisture for the old coefficients compared to the new coefficients. The original coefficients lead to large drying increments in the lower troposphere, as well as dramatic jumps at the when the satellites change. NOAA10 data starts at the end of Oct 86, and a couple channels drop out for several days in January 1987. The new coefficients work much better with the radiance assimilation, and the increments are much more uniform.

New JRA-25 Citation

Subject: JRA-25 paper
Date: Thu, 09 Aug 2007 13:02:25 +0900
From: JRA-25 user administrator

Dear JRA-25 reanalysis data users,

Sorry the address written in the previous mail was wrong.
Please refer the next address.

JRA-25 standard reference paper titled 'The JRA-25 Reanalysis' is aviailable from
http://www.jstage.jst.go.jp/article/jmsj/85/3/369/_pdf/-char/ja/

Please forward this mail to researchers who are interested in reanalysis.

Sincerely yours,

Secretariat of JRA-25
CPD/Japan Meteorological Agency

2004 Precipitation

Precipitation from a couple cases has been posted previously. Below are the monthly mean differences from GPCP for the GEOS 5 2004 January and July validation experiment, compared with JRA25 and NCEP. Qualitatively, the patterns of difference maps seem quite similar among the reanalyses. Though, the GEOS5 US precipitation seems low where the others are high. The GEOS5 tropics are also lower than the other reanalyses, a positive result. It is interesting to note that all the reanalyses underestimate GPCP in Europe in January. GPCP applies a snow undercatch correction, especially in Europe, which increases the precipitation. The GEOS5 southern hemisphere and North Atlantic biases appear quite low compared to the other reanalyses.

To expand on the comparisons, we are also producing Taylor Diagrams (Taylor 2001) for precipitation. The preliminary figures follow the mean difference maps. These show the spatial correlation of the reanalysis compared to the normalized standard deviation. The reference data set is GPCP, so the reanalyses are correlated to GPCP, and the standard deviation is normalized to GPCP. The closer the point is to 1:1, the closer a match to the reference data set. Here, CMAP is also included to show a portion of the uncertainty in the observations. By this metric, GEOS5 is improving the spatial pattern on the monthly precipitation. Spatial resolution of the analysis likely plays a role.

A more detailed examination of these statistics is underway, including more localized regions (such as the NA and SA continent). These relate to the monthly precipitation. The higher frequency precipitation is also being examined, compared to TRMM 3b42 3 hourly 1/4 degree. There is some uncertainty with those observations (satellite swaths are occasionally apparent in the spatial structure), but the comparisons so far have been promising where weather systems are reasonably reproduced.



Monthly mean precipitation differences from GPCP for NCEP R1, R2 , GEOS5 and JRA25. January 2004 (above) July 2004 (below).
Monthly Taylor diagrams for precipitation for the globe, NH, SH and Tropics using GPCP as a reference data set for NCEP R2 , GEOS5, JRA25 and CMAP. January 2004 (above) July 2004 (below). The normalized standard deviations increase with radial distance from the origin. All standard deviations are normalized to GPCP so that a value of 1.0 matches GPCP. Spatial correlation are plotted as the radial lines, so that the 1,1 point is identical to GPCP.