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Twenty-One Years of Verification from the HIRLAM NWP System

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Twenty-One Years of Verification from the HIRLAM NWP System 270 WEATHER AND FORECASTING VOLUME 28 Twenty-One Years of Verification from the HIRLAM NWP System KALLE EEROLA Finnish Meteorological Institute, Helsinki, Finland (Manuscript received 5 July 2012, in final form 17 September 2012) ABSTRACT The High-Resolution Limited-Area Model (HIRLAM) international research program maintains a synoptic-scale NWP system. At the Finnish Meteorological Institute, the HIRLAM system has been run operationally since 1990. The HIRLAM forecasts from 1990 to 2012 have been verified against the numerical analysis. In 2-day forecasts, the monthly rms error of the mean sea level pressure has decreased from about 4 to about 2 hPa; that is, the error is now about half of the value it was in the early 1990s. Similar reduction is seen in the 500-hPa height. The negative bias has decreased significantly. In addition, the dependence on the weather regime, measured as the correlation between the North Atlantic Oscillation (NAO) index and rms error, has decreased. The reason for these improvements can often be attributed to changes in the HIRLAM system. A single improvement, improving most significantly the forecast skill, is the rerun concept, which improves the HIRLAM first guess by utilizing the high-quality ECMWF analysis. Verifying against obser- vations or against the initial analysis gives similar results for a 48-h forecast. For a 6-h forecast, however, the field verification gives lower rms error values and lower bias values. In summary, the results indicate that the goal of the HIRLAM program has been fulfilled: to develop and maintain an up-to-date NWP system for 1- and 2-day forecasts on a limited domain. 1. Introduction 1990. Since then, the HIRLAM system has been run at FMI on a routine basis. The verification system against The High-Resolution Limited-Area Model (HIRLAM) the HIRLAM numerical analysis, hereafter called field international research program is a cooperative research verification, was implemented in June 1990. These ver- effort among 10 European meteorological institutes. The ification data up to February 2012 form the basis of this aim of the program is to develop and maintain numerical study. short-range weather forecasting systems for operational Jolliffe and Stephenson (2003) mention three reasons use by the participating meteorological institutes. The for verification. Their general headings are administra- program was initiated in 1985 and is on going. The first tive, scientific, and economic. First, administrative ver- numerical forecasting tool developed by the program is ification monitors the overall quality of the forecasts and called the HIRLAM system. It is intended to be used at is normally done operationally on a regular basis. This horizontal scales down to 5–10-km horizontal grid spac- information is used, for instance, to judge the needed or ing. It is a complete numerical weather prediction (NWP) proposed human or financial investments. Normally, the system, containing a data assimilation system, a limited- information is condensed down to a small number of area forecasting model with a comprehensive set of scores or numbers describing the essential features of physical parameterizations, and pre- and postprocessing the forecast quality. Second, in scientific verification, the of observations and forecasts. The first HIRLAM ver- keyword is understanding. By verifying the forecasts in sion (HIRLAM 1) was developed during 1985–88. The versatile ways, the weaknesses and deficiencies of the Finnish Meteorological Institute (FMI) was the first in- forecasting system can be detected and understood. This stitute to introduce it into operational use in January will then direct the research and possibly improve the forecasting system. Normally, a large variety of tools and methods are needed and they may vary from case to Corresponding author address: Kalle Eerola, Finnish Meteoro- logical Institute, Erik Palme´nin Aukio 1, P.O. Box 503, FI-00101 case. However, certain standard scores are used on a Helsinki, Finland. regular basis. For instance, the European Centre for E-mail: kalle.eerola@fmi.fi Medium-Range Weather Forecasts (ECMWF) monitors DOI: 10.1175/WAF-D-12-00068.1 Ó 2013 American Meteorological Society Unauthenticated | Downloaded 09/24/21 07:40 PM UTC FEBRUARY 2013 E E R O L A 271 the systematic errors in their forecasting system regularly described in section 3. Special emphasis is given to en- (Jung 2005). Statistical verification scores, like those in sure that the two datasets together form a continuous this study, are one tool in a toolbox of scientific verifi- and homogeneous time series. This section also contains cation. They give a measure and geographical distribu- a description and a discussion of the verification scores. tion for the error, but they do not reveal the reasons for Section 4 describes the results. Section 5 discusses the the errors. Thus, they are the first step in the process of role of developments in improving the forecasts and also revealing and understanding weaknesses and deficiencies the role of weather type in the forecast skill. Also, a com- in the forecasts. The third category, economic verifica- parison of field verification and observation verification tion, looks at the quality of forecasts more from the is discussed. Finally, a summary is given in section 6. customer’s point of view and is not discussed in this study. This study focuses mainly to the first category, but 2. A brief history of the HIRLAM forecasting also partly covers aspects belonging to the second category. system Research in all areas of NWP has been active during the last 20 years, both inside the HIRLAM program and The HIRLAM program maintains a standard version worldwide. At the same time, advances in computer of the HIRLAM forecasting system, called the HIRLAM science have made it possible to run more sophisticated reference system. It is a well-tested complete NWP and higher-resolution models. A natural question then forecasting system, containing everything that is needed arises: How are these advances reflected in the quality of for its operation: code, scripts, libraries, and tools. The the numerical forecasts? A long time series of verifica- FMI HIRLAM system follows the developments in the tion scores, computed in a uniform way, reveals the HIRLAM reference system and is regularly upgraded, overall advance in the quality of the forecasts, thus when a new reference system is released. In addition to helping to answer the following question: Have the in- the major developments described in this section, nu- vestments to research and operations of NWP been merous smaller upgrades, tunings, and corrections have worth doing? More broadly, has the HIRLAM program been made. succeeded in one of its main goal to develop and main- Table 1 gives the characteristic features of the differ- tain an up-to-date synoptic-scale NWP system? Since ent HIRLAM versions at FMI, including the horizontal 2004, FMI has been the so-called Regular Cycle of the and vertical resolutions, the number of grid points, and Reference model (RCR) center within the HIRLAM the times they became operational. The first column gives program, meaning that FMI runs the latest well-tested the acronyms used throughout this study. In February and accepted version of the HIRLAM system (reference 2004, FMI became the RCR center in the HIRLAM system) with operational status. This gives more weight program and since then it has been running the latest to the current results in judging the success of the reference system with minimal changes as its operational HIRLAM program. NWP system. Thus, the results since 2004 reflect the The verification dataset of this study consists of monthly quality of the HIRLAM reference system. field verification scores from the FMI HIRLAM fore- The first version (FIN), implemented operationally at casts from June 1990 to February 2012. Field verification FMI in January 1990, consisted of a limited-area version limits the meteorological parameters to be verified to of the ECMWF optimal interpolation data assimilation those analyzed by the data assimilation system. Impor- method (Lorenc 1981), a nonlinear normal mode ini- tant weather elements, such as precipitation, cloudiness, tialization method (Machenhauer 1977), and a limited- and 2-m temperature, cannot directly be verified with area version of the ECMWF hydrostatic primitive equation this concept. This study concentrates on mean sea level gridpoint forecast model (Burridge and Haseler 1977). pressure (MSLP) and geopotential and temperature on The physical parameterizations contained horizontal and constant pressure levels. The verification scores are the vertical diffusion, radiation, surface processes, and con- two conventional scores: the root-mean-square error vective and stratiform precipitation. All of them, except (rms error) and the mean error (bias). Although every radiation, were based on the first ECMWF global model meteorological center running a NWP system also ver- (Tiedtke et al. 1979). ifies their forecasts, there are not many published studies The development of three- and four-dimensional about the long-term behavior of the forecast quality. variational data assimilation methods (called 3DVAR The structure of this article is the following. Section 2 and 4DVAR, respectively) started in the 1990s. In March describes the history of the HIRLAM system by high- 2003, the 3DVAR system (Gustafsson et al. 2001; Lindskog lighting the major scientific developments during the et al. 2001) was ready to be implemented at FMI as latest 20 years. The data in this study consist of two a part of the ATX version. The first step toward 4DVAR datasets. These datasets and their further processing are data assimilation (taking time into account) was the Unauthenticated | Downloaded 09/24/21 07:40 PM UTC 272 WEATHER AND FORECASTING VOLUME 28 TABLE 1. Some characteristic features of the different synoptic-scale HIRLAM systems at FMI. Here, nx and ny are the number of grid points in the x and y directions, respectively, and dx is the horizontal resolution (8).
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