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Assimilation of GOES-16 Radiances and Retrievals Into the Warn-On-Forecast System MAY 2020 J O N E S E T A L . 1829 Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System THOMAS A. JONES,PATRICK SKINNER, AND NUSRAT YUSSOUF Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, and University of Oklahoma, Norman, Oklahoma Downloaded from http://journals.ametsoc.org/mwr/article-pdf/148/5/1829/4928277/mwrd190379.pdf by NOAA Central Library user on 11 August 2020 KENT KNOPFMEIER AND ANTHONY REINHART Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma XUGUANG WANG University of Oklahoma, Norman, Oklahoma KRISTOPHER BEDKA AND WILLIAM SMITH JR. NASA Langley Research Center, Hampton, Virginia RABINDRA PALIKONDA Science Systems and Applications, Inc., Hampton, Virginia (Manuscript received 14 November 2019, in final form 28 January 2020) ABSTRACT The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-mm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill. 1. Introduction (e.g., Derber and Wu 1998; McNally et al. 2000, 2006; Auligné et al. 2011; Zhu et al. 2016). As new satellites Assimilation of satellite data into global numerical and sensors are launched, the additional data has con- weather prediction (NWP) models has led to substantial tinued this trend in increasing forecast skill to this day. forecast improvements during the past two decades While satellite data has proven to be a vital tool in global NWP, its impact to high-resolution, regional NWP Corresponding author: Dr. Thomas A. Jones, thomas.jones@ systems is still being assessed. Data assimilation into noaa.gov regional models such as the High-Resolution Rapid DOI: 10.1175/MWR-D-19-0379.1 Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). 1830 MONTHLY WEATHER REVIEW VOLUME 148 Refresh (HRRR) contains many challenges not present in systems (Aksoy et al. 2009, 2010; Dowell et al. 2011; global systems. The HRRR runs over a North American Yussouf et al. 2013, 2015; Wheatley et al. 2015; Johnson domain at a 3 km horizontal resolution with hourly data et al. 2015; Wang and Wang 2017). However, satellite assimilation cycling (Benjamin et al. 2016; Alexander et al. data samples nonprecipitating clouds and environmental 2018). At these temporal and spatial resolutions, many of conditions not readily sensed from radars. Assimilating the assumptions applied in global satellite data assimila- these data provides this information to the model analy- tion, such as error correlation, data thinning, quality con- sis, often improving forecasts (Jones et al. 2015, 2016). trol and handling of outliers are not necessarily applicable Satellite data are also useful in providing information on the convection allowing domain. In particular, the poor on convection in the absence of radars, but a truly spatial data coverage of polar orbiting sensors, which have successful WoFS system does require reasonable ra- Downloaded from http://journals.ametsoc.org/mwr/article-pdf/148/5/1829/4928277/mwrd190379.pdf by NOAA Central Library user on 11 August 2020 the greatest impact in global models, significantly limits dar data coverage to reliably generate consistently their potential impact in higher resolution regional models. skillful forecasts. Data from geostationary satellites such as the Advanced Satellite data comes in many forms and can be assimi- Baseline Imager (ABI) on board the GOES-R series are lated as radiances (brightness temperatures) or retrievals. much more suitable to the requirements of these systems. Each method has its advantages and disadvantages, but The ABI samples far fewer channels than polar orbiting both convey important environmental and cloud prop- hyperspectral sounders (16 versus ;1000), but the chan- erties to the data assimilation system. Jones et al. (2015, nels it does sample have important sensitivities to atmo- 2016) assimilated cloud water path (CWP) retrievals spheric temperature and moisture properties and are into the WoFS, and showed improvement in the fore- available at a 2 km horizontal resolution every 10–15 min casting of cloud properties, convective initiation, and the with a data latency on the order of a few minutes (Schmit near-storm environment compared to experiments that et al. 2005). The low data latency is very important for the only assimilated radar data. Similar results were obtained Warn-on-Forecast System (WoFS), which assimilates data by Zhang et al. (2018) through assimilating all-sky water at 15 min intervals (or less) over a regional domain in a vapor channel radiances. Jones et al. (2018) experimented real-time fashion to generate short-term (0–6 h) forecasts with assimilating GOES-13 6.95 mm clear-sky water vapor of high-impact weather events (Stensrud et al. 2009, 2013; channel radiances in combination with radar and CWP Gallo et al. 2017; Choate et al. 2018). and showed that assimilating radiances did improve the In recent years, many studies have been performed to model analysis when compared against observations. assess the suitability of geostationary satellite products This translated to improvements in the forecasting of into convection allowing models, which have shown great rotating severe storms, but the correction of inherent promise (e.g., Szyndel et al. 2005; Vukicevic et al. 2006; model biases caused a degradation in one case. Stengel et al. 2009; Polkinghorne et al. 2010; Polkinghorne With the operational availability of GOES-16 data and Vukicevic 2011; Otkin 2012a,b; Qin et al. 2013; Zou and the increasing maturity of the WoFS, a comprehen- et al. 2013, 2015; Jones et al. 2013, 2014, 2015, 2016; Zhang sive analysis of the relative impact of CWP retrievals, et al. 2016; Minamide and Zhang 2019; Honda et al. clear-sky, and all-sky radiances is necessary. However, 2018a,b; Zhang et al. 2018; Okamoto et al. 2019; F. Zhang the ideal set of observations to assimilate remains an open et al. 2019; Y. Zhang et al. 2019). One key advantage of question and this work hopes to provide some answers satellite data is its availability in regions where other using several high-impact severe weather events run in surface and radar observations are not reliably present. real time during spring and summer 2019. In particular, Thus, many of these studies have focused on assimilating comparing the results from assimilating retrieved cloud geostationary satellite data to improve hurricane track properties versus all-sky radiances is necessary to deter- and intensity forecasts (e.g., Zou et al. 2015; Zhang et al. mine the advantages and disadvantages of both obser- 2016; Minamide and Zhang 2019; Honda et al. 2018a,b; vation types. Migliorini (2012) found that in the end, both F. Zhang et al. 2019). Others have focused on severe contain a similar information content, but their obser- weather prediction over land when other data sources vation characteristics differ significantly, which can have are not available (e.g., Zhang et al. 2018). Finally, several large impacts during the assimilation processes. For studies assimilated satellite data in concert with other example, assimilating retrievals or radiances associated high-resolution datasets such as radar reflectivity and with upper-level cirrus clouds both add positive incre- radial velocity to complement the advantages of each to ments to frozen hydrometeor values. However, the increase skill in high-impact weather prediction (Jones magnitude of these increments can vary substantially et al. 2013, 2015, 2016, 2018; Y. Zhang et al. 2019). along with the impact to specific hydrometeor variables. Assimilation of surface-based radar reflectivity and ra- Testing showed that assimilating CWP observations dial velocity observations forms the basis for WoFS-like had the largest impact on snow concentrations while MAY 2020 J O N E S E T A L . 1831 assimilating radiances had the largest impact on ice con- each assimilation cycle can be used in updating the centrations in the same atmospheric layer (not shown). model state (Whitaker et al. 2008). The WoFS cycles at An experiment that assimilates only radar observa- 15 min intervals beginning at 1700 UTC until 0300 UTC tions will be used as a baseline from which the value of assimilating all available conventional, radar, and sat- satellite data will be assessed.
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