GFDL - Geophysical Fluid Dynamics Laboratory

Reanalysis Ideas — New Ideas (Jan 2012)


Some Ideas For Future Reanalysis Efforts

John Lanzante

 


New Ideas (Jan 2012)


Minimal Steps That I Believe Will Be Necessary


  1. Select which raw input datasets to use (e.g., radiosonde, satellite, sfc T, SLP, cloud, etc.). This might be based on what is known about the quality and homogeneity of each dataset, it’s temporal and spatia extent, and model-based experiments conducted to see how useful or influential each dataset is. For example, one might conduct experiments like the Hadley Centre did for HadAT2 in which climate model output from historical runs is used to create artificial data with the characteristics of each data type (radiosonde, satellite, ISCCP, OWS, etc.). These data would be sampled spatially and temporally the same as the real counterpart, and would have inhomogeneities introduced mimicking reality. One could then ingest these data into the reanalysis system, withholding different types one at a time, and see how it affects the ability to perform steps 4 and 5 (below), and ultimately how well trends or other low frequency variability can be recovered from the final product. It may turn out that some data types do more harm than good and should just be excluded. In addition to selecting particular datasets, here one would also chose which station records or satellites, etc. would be used. For example, some stations might have very short records, and others with longer records that do not span the full period may be close to data rich areas, and some satellites may have short records or little overlap with nearby satellites in the sequence; it may turn out better to simply exclude these incomplete records. Model/assimilation experiments can be used to determine which to keep and which to discard.
  2. Determine the best way to use multiple versions of each input dataset. For example, there are 5 homogenized radiosonde, 3 satellite and 3 surface for temperature. There are also unhomogenized versions of each. Should all available datasets be ingested, just some, or the one “best”? Should each of these inputs be homogenized by the reanalysis system and then ingested or compared to see which is “best”?
  3. Use the reanalysis system to homogenize each input data type (radiosonde, satellite, surface, etc.), even if the input has been homogenized via other means. In this step, one particular data type (e.g., radiosonde T) will be homogenized by excluding this type and ingesting other types of data (e.g. sfc T, SLP and radiosonde winds) into the reanalysis system. The purpose is to create a reference series, which will be used to homogenize the excluded data type. This process will be repeated, in turn for each data type (i.e., exclude only the type for which a reference series is desired). At the conclusion of this step, each input data type will have been homogenized using reanalysis-generated reference series.
  4. Perform “final” reanalyses using the homogenized data sets created in step 3. There will necessarily be several “final” versions, varying by the types of input. One may contain all data inputs (determined from step 1). Other versions may include or exclude certain types. For example, one version might be based on sfc and radisonde data,but no satellite data at all. Another version might use just satellite data along with sfc data (and of course would be limited to the satellite era). Other versions might exclude or include cloud, humidity, OWS, etc. The notion here is that just as we currently have multiple homogenized radisonde and satellite datasets, none of which can be unambiguously declared as the “best”, we might have multiple climate reanalyis products. Depending on the application, the user might have to use and compare results from several of these products, although some users might just be interested in the one version based on the maximal amount of data.
  5. For output datasets from step 4 it will be necessary to perform an additional form of homogenization related to datasets that do not span the entire period of record. For example, since satellite T starts in 1979, there is a potential discontinuity in 1979 from the sudden introduction of these data into the input stream. One way to deal with this would be to examine a version of the reanalysis based only on radiosonde data, and use it to derive adjustments that need to be applied at 1979. There will be multiple corrections, to account for different datasets (OWS, MSU, etc.) that don’t span the entire period of record.

The Final Output Will Consist Of


  1. The one “best” version of climate reanalysis (based on the most complete set of suitable inputs) generated in steps 4/5.
  2. Several alternate versions of climate reanalysis, based on more limited inputs, from steps 4/5.
  3. The homogenized versions of the inputs created in step 3. These will potentially represent the successors to GISS/NCDC/CRU for sfc T, RATPAC/HadAT2/IUK/RAOBCORE/RICH for radiosonde T, and UAH/RSS/STAR for satellite T. For many other data types the benefits will be even greater, as no homogenized products currently exist.

What Will It Take To Get This Accomplished?


  • In my opinion all of this is feasible. It will require considerable resources, human and computing, and cross-collaboration amongst disparate communities with different areas of expertise. Three main areas of expertise are needed:
    1. Analysis/Assimilation/Modelling Since the initial NCEP/NCAR effort much has been learned, both at NCEP and other institutions around the country and world regarding how to do this.
    2. Handling/Processing Of Multiple Large Data Sets Likewise the expertise, such as that provided by NCAR for the first US reanalysis would be required.
    3. Data Homogenization Unlike other 1st and 2nd generation reanalyses, this community would play a central role. There would necessarily need to be much back and forth interaction between these folks and those from (2) and especially (1).
  • There would be many new types of hurdles that would have to be overcome that were not pertinent to 1st and 2nd generation renanalyses, but I believe could be accomplished. The biggest impediment is obtaining, large, long-term funding, not any easy task given the current economic and budgetary situation in the US and around the world.
  • I would equate the completion of the 1st NCEP/NCAR reanalysis to that of landing humans on the moon. It was a wondrous accomplishment, with incredible benefits that seemed like fantasy only a generation earlier. At the time of the first landing on the moon, many people probably envisioned colonization of the moon and landing humans on Mars as almost certainly occurring in the next generation. Unfortunately, human space exploration has not advanced much since then and does not seem likely anytime soon. The question is, will the realization of a true Climate Reanalysis suffer the same fate?

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