WINTER STORM RECONNAISSANCE NCEP ENSEMBLE FORECAST PRODUCTS EXAMPLE PUBLICATIONS FURTHER STUDIES TOST EXPERIMENTS    WC-TOST ATReC NORPEX-98 FIELD EXPERIMENTS RESEARCH LINKS - SHARAN MAJUMDAR WINTER T-PARC (2009) SUMMARY OF RESULTS

HISTORY Adaptive observations has a relatively long history within NOAA. The Hurricane Reconnaissance (HR) program, in which NOAA and US Air Force (USAF) planes were initially tasked to collect critical information on the location and intensity of hurricanes, started in 1947. In 1982, NOAA's Hurricane Research Division, then National Hurricane Research Laboratory, began research flights in the data-sparse regions around tropical cyclones in order to improve numerical model forecasts of their tracks. Papers back to 1920 (Gregg 1920; Bowie 1922) suggest that observations to the northwest of the tropical cyclone center are most important for subsequent forecasts (Franklin et al. 1996). This was confirmed during subjectively planned synoptic flow missions. Burpee et al. (1996) found that such flights allowed for an improvement in hurricane track forecasts of approximately 25%, and as a result, NOAA procured the Gulfstream-IV (G-lV) plane for operational synoptic surveillance flights for hurricanes threatening landfall in the United States and its territories east of the dateline. Targeted observations, where data are collected in specific areas at specific times with the aim of improving the quality of pre-selected Numerical Weather Prediction (NWP) forecast features has only a short history (Toth et al. 2001). The idea was first discussed publicly at a workshop in 1995 (Snyder 1996). Related research at several NWP centers has first organized around a major field program, the Fronts and Atlantic Storm Track Experiments (FASTEX, Joly et al. 1999). NCEP, in collaboration with Pennsylvania State University (PSU) scientists, contributed by developing the Ensemble Transform technique (ET, Bishop and Toth 1999), later superseded by the Ensemble Transform Kalman Filter technique (ETKF, Bishop et al. 2001; Majumdar et al. 2002), along with adjoint and quasi–inverse linear techniques (Pu et al. 1997). In 1997 these techniques were used, in a collaborative effort with Massachusetts Institute of Technology (MIT) scientists, in real time during the FASTEX field experiments to identify observational areas for the release of dropsondes by manned aircraft. Results from the use of these adaptive observational techniques. in FASTEX are reported in Toth et al. (1998), Szunyogh et al. (1999a), and Pu and Kalnay (1999). The ET technique was further tested, along with an adjoint technique developed by the Naval Research Laboratory (NRL), in the following winter within the North Pacific Experiment (NORPEX, Szunyogh et al. 1999b; Langland et al. 1999). NCEP also participated in the California Landfalling Jets Experiment (CALJET, Ralph et al. 1998), and later in the Pacific Landfalling Jets Experiment (PACJET) regional field programs by providing experimental mesoscale adaptive observational guidance (Toth et al. 2000). The success of these early field experiments led to the establishment of the Winter Storm Reconnaissance field program in 1999 (Toth et al. 1999).The aim of the WSR program is to reduce forecast errors for significant winter weather events over the contiguous US and Alaska in the 24–96 hour lead time range through the use of adaptive observations over the data sparse northeast Pacific. For this purpose dropsonde data are collected by the Aircraft Operations Center (AOC) of the National Oceanic and Atmospheric Administration (NOAA) and the 53rd Weather Reconnaissance Squadron of the US Air Force (USAF), through the use of manned aircraft operating out of Honolulu, HI, and Anchorage, AL. Verification results indicate that the majority of the targeted forecasts are significantly improved (Szunyogh et al. 2000; 2001). Based on these results WSR became a regular program (Toth et al. 2001). Recognizing the success and importance of wintertime adaptive observations, in January 2001 NWS made the WSR program operational.