J2.1 REANALYSIS BEFORE RADIOSONDES USING ENSEMBLE DATA ASSIMILATION Jeffrey S. Whitaker∗ ,Gilbert P. Compo, Xue Wei and Thomas M. Hamill NOAA-CIRES Climate Diagnostics Center, Boulder, CO 1. INTRODUCTION observing network, created by sub-sampling the surface pressure observations for 2001. We focus on surface pres- A primary goal of climate change research is to under- sure observations since they are the most widely available stand variations in the frequency and intensity of severe and reliable observations in the early 20th century, and weather events on decadal and longer time-scales. An provide more information about the state of the free tropo- obvious prerequisite for achieving this goal is an accu- sphere than surface wind and temperature observations. rate baseline estimate of the frequency and intensity of In a companion report we will carefully examine the avail- severe weather over the last century. Analyses of long- able data record for the last 150 years and assess the per- term changes in extra-tropical cyclone frequency and in- formance of several analysis schemes with surface-only tensity have been hampered by the inadequacy of cur- observation networks representative of 1890 to 1940. rent datasets (IPCC 2001, p. 163). Since synoptic-scale weather systems have time-scales of less than a week, Previous studies using idealized ensemble data assimi- a century-long dataset of tropospheric circulation fields at lation systems (e.g. Hamill and Snyder 2000) have shown daily resolution is required. The NCEP-NCAR 50-year re- that their flow-dependent background-error covariances analysis (Kistler and Coauthors 2001) provides four times are most beneficial when the observing network is sparse. daily gridded circulation fields beginning in 1948, when When observations are very dense, the background-error digital upper-air observations were widely available. The covariances do not change as much from time to time, only daily tropospheric circulation dataset available that so static background-error covariance models (such as extends back before 1948 is derived from charts of sea- used in 3DVar) can be nearly as effective. In addi- level pressure hand-drawn by U.S. Air Force meteorolo- tion, the computational cost of computing analysis incre- gists in the 1940’s and 1950’s (United States Weather Bu- ments in recently proposed ensemble-data assimilation al- reau 1944). Although a remarkable achievement for its gorithms (Houtekamer and Mitchell 2001; Whitaker and time, this original reanalysis suffers from serious problems Hamill 2002) is directly proportional to the number of ob- associated with incorrect assumptions made by the ana- servations being assimilated. Therefore, ensemble-based lysts in data sparse regions (Jones 1987; Trenberth and data assimilation should be more computationally feasible Paolino 1980) and does not provide estimates of the full and provide the greatest benefit over current operational three-dimensional tropospheric structure. Clearly, a bet- schemes in situations when observations are sparse. Re- ter dataset is needed - but is it possible to create a more analysis before the radiosonde era is just such a situation. accurate daily tropospheric circulation dataset for the first half of the 20th century given the paucity of available ob- The paper is organized as follows: section 2 contains servations? a description of the experimental design, including the In this study we examine whether advanced data assim- simulation of the 1915 observing network, the ensemble ilation systems have significant advantages over currently data assimilation system and forecast model. Section 3 available systems for sparse networks of surface pressure presents the results of the assimilation experiments, which observations representative of the early part of the 20th show that the EnSRF can produce mid-tropospheric anal- century. Specifically, we examine the performance of the yses given surface observations at 1915 densities which ensemble-based data assimilation system described by are as accurate as 2.5 day forecasts are today. Section 4 Whitaker and Hamill (2002) applied to a simulated 1915 contains a summary of the results and a discussion of the unresolved issues that need to be addressed before these ∗Corresponding author address: Jeffrey S. Whitaker, NOAA Climate Diagnostics Center, R/CDC1, 325 Broadway, Boulder, CO 80305; email: techniques can be applied to a real reanalysis of the first [email protected] half of the 20th century. 2. Experimental Design assimilated, they were used to reduce the surface pres- sure observation to the model orography as discussed in a. The Observations the next paragraph). A map illustrating a typical simulated surface pressure network at 00 and 12 UTC is shown in To simulate how a modern data assimilation system can the bottom hand panel of Figure 4. In this example there be expected to perform on a historical observational net- are 204 surface pressure and temperature observations in work, the 2001 observational network was reduced to only the Northern Hemisphere poleward of 20oN. At 06 and 18 surface observations with a density typical of 1915. The UTC, the number of surface marine observations is nearly number of synoptic observations potentially available for the same, but there are almost no observations over land each month during the period 1913-1917 was determined areas. in 5x5 degree boxes from a detailed inventory of the digital land surface data holdings of the National Center for At- mospheric Research (NCAR), the National Climatic Data Center (NCDC), the Waves and Storms dataset (Schmith et al. 1997), and manuscript data holdings of NCDC. The Global Historical Climate Network (GHCN) surface pres- sure station locations were used a proxy for synoptic re- ports currently available only in manuscript form, some of which are now being digitized by NCDC (S. Doty, per- sonal communication), Environment Canada (V. Swail, personal communication), the European Union (P. Jones, personal communication) as well as other international ef- forts (R. Jenne, personal communication). The marine ob- servations available were also determined in 5x5 degree boxes from a detailed inventory of ICOADS Release 2.0 Figure 1: Number of surface pressure observations (per (Woodruff et al. 1998; Diaz et al. 2002), the German Ma- day) available in the Northern Hemisphere poleward of rine Meteorological archive (courtesy of V. Wagner), and 20oN each year from all available digital sources, including the Kobe Collection 2001 (Manabe 1999). The 1915 ob- those currently being digitized. servation network was chosen for this study since it is rep- resentative of data availability during the earlier part of Observational error standard deviations were the same the 20th century. The number of surface observations in- as those used in the NCEP-NCAR reanalysis, 1.6 hPa for creases dramatically in the 1930’s and 1940’s. ship observations and 1 hPa for land stations. In situations The quality-controlled observations used as input to the where the absolute difference between the model orog- NCEP-NCAR reanalysis (Kistler and Coauthors 2001) for raphy and the real orography is less than 600 meters at 2001 were sub-sampled to simulate the 1915 network. the observation location, and a co-located temperature ob- The 2001 observations were first reduced by retaining only servation is available, the surface pressure observation is surface pressure observations from radiosonde and ma- reduced to the model orography assuming the mean tem- 1 Γ∆ rine reports issued within 30 minutes of the analysis time. perature in the intervening layer is Tob+ 2 z, where Tob is o 1 The location of the radiosonde stations gives an excel- the temperature observation, Γ is 6.5 Kkm− and ∆z is lent approximation to the location of historically available the difference between the model and− real orography. The land surface pressure stations. This reduced the total net- observation error is adjusted accordingly, assuming that o 1 work from over 150,000 observations to less than 2000 per the error in the estimate of the lapse rate Γ is 3 Kkm− . analysis. The simulated historical network was then con- If ∆z > 600 m then the surface pressure observation is structed by randomly selecting from the reduced network not| used.| If a co-located temperature observation is not in each five-degree box with a probability equal to the av- available, the surface pressure observation is used with- erage number of daily historical surface pressure observa- out modification if ∆z < 10 m, otherwise it is not used. tions in the box normalized by the average number of daily To assess the benefit| | of flow dependent background- surface pressure observations in the reduced 2001 net- error covariances, the analyses produced by the ensemble work. Figure 1 shows a map of the probabilities assigned data assimilation system are compared to those produced to each five-degree box. Surface temperature observa- by two other simpler systems with static background-error tions were included with each surface pressure observa- covariance estimates. The 3DVar system used to produce tion (although surface temperature observations were not the NCEP-NCAR reanalysis, a.k.a the Climate Data As- 2 similation System, or CDAS (Kistler and Coauthors 2001), with the NCEP-NCAR reanalysis for 2001. Because the was adapted to the 1915 observation network by multiply- reanalysis used several orders of magnitude more obser- ing the background-error covariances used in the reanaly- vations, including radiosondes, aircraft and satellite sound- sis by a constant factor > 1, and turning off the divergence ings, we expect that this difference is significantly larger tendency constraint. We call this modified CDAS system than the error in the reanalysis itself. CDAS-SFC. Increasing the background-error covariance amplitude b. The Ensemble Data Assimilation System in the CDAS-SFC system was necessary since the Ensemble data assimilation systems transform a fore- background-error covariances used in CDAS were tuned cast ensemble into an analysis ensemble with appropri- to the modern observing network (Kistler and Coauthors ate statistics.