Utilizing Increments in Model Development

MERRA data has complete budgets for water and energy, including the incremental analysis updates (IAU) that constrain the model forecast to the analyzed observations. The increments can be interpreted instantaneously (at the six hour analysis) as a representation of the forecast error, or for longer terms as the mean model bias. The magnitude of the IAU terms are not trivial, and should be accounted for, certainly in budget studies, but can also be useful in understanding the representation of weather and climate phenomena in reanalyses.

As an example of utilizing the increments to evaluate the background model, Mapes and Bacmeister (2012) have evaluated MERRA's tropical climate and convection, relating significant IAU values to weaknesses in the representation of physical processes. They suggest diagnostics and potential areas for model development.

Extrapolation to P > Ps

As many have found, MERRA pressure level data does not provide values for pressure surfaces when they are greater than the surface pressure (e.g. high topography).  Other reanalyses extrapolate the data using the surface meteorology and assumed lapse rates. This data may be useful in some cases such as zonal averaging, stream functions and thickness calculations.

A recent post at reanalysis.org provides user developed codes to fill these undefined grid points. This should be useful as one could adapt the codes to the filling method applied in other reanalyses to better match their extrapolated data.

See Extrapolation of MERRA Reanalyses to obtain continuous fields for more information.

Reanalysis Conference Report

The Report of the 4th International Conference on Reanalyses (May 7-11, 2012, Silver Spring MD) is presently available from the World Climate Research Programme.

Aircraft Temperatures

Early in the MERRA reanalysis period, aircraft observations are sparse, but increase in time, eventually providing a significant amount of conventional observations. Cardinali et al. (2003) identified biases in aircraft temperature observations, and Ballish and Kumar (2008) further examined the biases in each type of commercial aircraft. Figure 1 shows an 2001-2009 mean bias between collocated aircraft and radiosonde 200mb temperature, over the United  States. Almost everywhere, aircraft are warmer than the radiosonde observations.

Figure 1 Collocated aircraft/RAOB at 200mb temperature (K) differences assimilated in MERRA  averaged from 2001-2012. (computed from the differences of each observaitons background departure)

Figure 2 shows the monthly mean difference between collocated radiosonde and aircraft observations assimilated in MERRA over the U. S., while the dots show the number of collocations (thou/yr).  While the data for these figures are binned and gridded, area and monthly averaging include weighting for the number of observations. Early in the reanalysis, there are lower numbers of aircraft observations, and the differences reflect that with more monthly variability. In 1990-1991, increasing number of observations increase the distribution of data, and the warm bias converges. After 1996, there is an exaggeration in the annual cycle, where the summer aircraft observations get even warmer. However, every month is a positive difference.

Figure 2 Time series of monthly mean differences of 200mb temperature collocations over the United States (Aircraft minus RAOB OmF, in red, K). The black dots indicate number of collocations each year (in thousands, right axis).

The increasing number of collocations reflects the increase in availability of aircraft observations. There are many more aircraft observations being assimilated away from the vicinity of the radiosondes. The number of observations then influences the data assimilation, where the analysis is drawn toward the aircraft data. Figure 3 shows the time series of background departure for collocated radiosonde and aircraft 200mb temperature. As the aircraft observations increase in number, their background departure decreases (this also holds for the RMS of the background departure).

Figure 3 Time series of monthly mean background departure (OmF) of the collocated RAOB (black) and Aircraft (red) 200mb temperatures (K, left axis). The black dots indicate number of collocations each year (in thousands, right axis).

Both Cardinali et al (2003) and Ballish and Kumar (2008) have suggested bias corrections for commercial aircraft temperature data, using more limited comparisons than these. ECMWF has implemented a bias correction in their forecast system.

Reanalyses trends

One of the most important topics and calculations in climate science is trend, aiming to determine long term changes. Significant issues exist in the observational record, and methods correcting the problems themselves need to be explained and verified. In a recent update to the U.S. Historical Climate Network (HCN) station data, Vose et al. compare the observational record against several reanalyses near surface air temperature and their ensemble. In looking at the continental United States, their Figure 1 shows the revised HCN trend is larger than the uncorrected data, but also remarkably close to the ensemble of the reanalyses. Also, despite the differences in trends of the reanalyses, there is very good agreement in the reanalyses interannual variability around the trends (their figure 3). The bottom line is that the corrections to HCN are in agreement with reanalyses (all are statistically significant warm trends), but it is noted that this is not a validation of the corrections.

The spatial distribution of reanalysis trend relative to the HCN trend shows substantial local variations among the reanalyses (their figure 4). So, while the observational forcing imposed on reanalyses can influence the large scale features, the model predictions used to make the analyses impart  some uncertainty related to the model physical parameterizations. If the model errors are random, the ensemble should then minimize the error. Errors that are systematic among all reanalyses would persist in the ensemble.

This paper demonstrates some important points about reanalyses. Any one reanalysis may have uncertainty in any given research project. Multiple reanalyses can help identify these uncertainties and perhaps the background model biases in the reanalysis. However, the reanalyses output variables being compared must have equivalent formulations to take advantage of the availability of the current modern reanalyses through such intercomparisons. Likewise, on hourly surface output in MERRA and CFSR were useful in this study.

Return of the MERRA Blog

As many readers will understand, the demands on time can be many, and there is never enough time to do all that you want to do. Being spread thin for some time, the MERRA blog fell into neglect. However, with a lot of interesting things going on, from research with MERRA to formulating plans for subsequent reanalyses (and noticing in the last month a fair number of hits from around the world), I will be trying hard to find time to make regular posts.

The original purpose of the blog was to 1) follow the development of the MERRA system and eventually the production of data and then 2) follow the research that was being done with MERRA and other reanalyses. #2 never fully materialized, but there is a lot of work being published on reanalyses lately.  So, for the near future, summaries of published research or perhaps topical discussions involving several papers/reports will likely be the regular topic. However, new reanalysis development is on the horizon, as well.

Mostly, I figure that the hits on the MERRA blog are coming from internet searches for hard to find information, now that the MERRA overview paper is printed. This is an important issue, as users need to know if a reanalysis is applicable to a research topic, and its strengths and weaknesses. So, perhaps this can open the communication, just a little more.

Presentations Available

The 4th WCRP International Conference on Reanalyses wrapped up Friday, May 11 with a panel discussion on agency priorities and a session on diverse uses of reanalyses and future directions. The presentations from the oral agenda are all available for download at icr4.org, and the poster presentations are coming in as well!

ICR4 Less than a week away

The 4th WCRP International Conference on Reanalysis begins in less than a week! The program and logistics are posted at ICR4.org. For anyone planning to attend but not yet registered, the registration fee increases substantially for on-site registration, so be sure to register before then!

Ancillary Land and Ocean data

Two data sets for Ocean and Land have been released through the GES DISC, derived from MERRA, but not included in the original data collection. The ocean data has 1 hourly fluxes, meteorology and stresses over both open water and ice. the MERRA-Land data is an offline reprocessing using MERRA atmospheric forcing with bias corrected precipitation to produce a new set of surface fluxes and land states (Reichle et al. 2011).

See the release page for more information.

Travel Support: 4thInternational Conference on Renalyses

Travel support applications for the 4th International Conference on Reanalyses are due Jan 27. Interested students and early career scientists should visit http://icr4.org/support.html for more information.

4th Reanalysis Conference, Deadline extension

CALL FOR ABSTRACTS (Deadline: EXTENDED 13 February 2012)

4th WCRP International Conference on Reanalyses
7-11 May 2012
Silver Spring, Maryland USA

The 4th World Climate Research Programme (WCRP) International Conference on Reanalyses provides an exciting opportunity for the modeling community to review and discuss the major observations and modeling research associated with reanalyses.

Conference Agenda:

Keynote Speaker: Adrian Simmons, ECMWF

Status and Plans: Major international reanalysis development, including broad disciplinary overviews (ECMWF, NCEP, JMA and GMAO)

Validation and Metrics: Intercomparison and validation studies; assessing the impact of the assimilation and analysis increments; innovative diagnostics that characterize the degree to which a reanalysis represents reality and ultimately applicability for weather and climate research. (Kevin Trenberth, Rolf Reichle, Arlindo Da Silva, others TBD)

Data Assimilation: Data assimilation techniques and impact on eventual reanalysis data products, especially producing a climate quality time series. (Dick Dee, others TBD)

Space and In Situ Observations: Studies on the quality and stewardship of observations and their use in reanalyses and exploiting new data types and sources. (Roger Saunders, others TBD)

Application in Support of Climate, Weather and Environmental Services: Innovative research using reanalysis to study the weather, ocean, hydrology and climate, including operational climate monitoring, study of extremes and high impact weather, climate assessment and end-to-end decision making studies. (Siegfried Schubert, others TBD)

International Collaborative Efforts: Projects and plans for developing and using reanalysis to the benefit of the international community.

Abstracts are submitted as a part of the registration process. During registration you will be asked to create a Username and Password, which will allow you to upload a revised abstract at any time and make changes to your registration. Note, that at the end of the registration process you can select an option to "Pay Later." If you choose to this option the registration fee will be based on the date of the payment according to the fee schedule at http://icr4.org/registration.html.

To submit an abstract, visit the Conference website at: http://icr4.org/about.html

Conference Deadlines:
Abstract Submission: 13 February 2012
Early Bird Registration: 13 February 2012
Hotel Reservations: 13 March 2012