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S. Mark Leidner
,
Lars Isaksen
, and
Ross N. Hoffman

Abstract

The impact of NASA Scatterometer (NSCAT) data on tropical cyclone forecasting on the European Centre for Medium-Range Weather Forecasts four-dimensional variational (4DVAR) data assimilation system is examined. Parallel runs with and without NSCAT data were conducted. The 4DVAR can use single-level data, such as scatterometer winds, to good advantage. The 4DVAR system uses data at appropriate times and has the potential to accurately resolve the ambiguity inherent in scatterometer data, by using a two-ambiguity cost function at each NSCAT location. Scatterometer data are shown to improve the depiction of the surface wind field in both tropical cyclones and extratropical lows, and can provide early detection of these features. Case studies of Hurricane Lili, and of Typhoons Yates and Zane (all in 1996), show major positive impacts of NSCAT data on forecasts of tropical cyclone intensity and position.

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S. Mark Leidner
,
David R. Stauffer
, and
Nelson L. Seaman

Abstract

Few data are available over the world’s oceans to characterize the initial atmospheric state in numerical models. Objective analysis in these regions is largely based on forecast fields obtained from a global model and used as the background (“first guess”). Unfortunately, global models often do not resolve the marine boundary layer (MBL) structure, which is important for simulating stratus clouds, coastal zone circulations, and electromagnetic wave propagation. Furthermore, initialization of the MBL in the coastal zone and data-sparse oceanic regions poses a challenging mesoscale modeling problem. The goal of this study, therefore, is to improve warm-season short-term mesoscale numerical prediction of California coastal zone meteorology by improving the model initial conditions in the coastal zone and offshore data-void regions. Initialization strategies tested include standard static and dynamic techniques and a new marine boundary layer initialization scheme that uses a dynamic initialization based on the remarkably consistent summertime marine-layer climatology of the eastern Pacific Ocean.

The model used in this study is the Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5). Experiments were performed for a typical summertime case (3–4 Aug 1990) to determine an initialization strategy suitable for coastal zone forecasting over the northeast Pacific. The meteorology in this case was dominated by quasi-stationary synoptic-scale high pressure over the ocean. Results from the model experiments were verified using 6-hourly coastal rawinsonde observations and visible range satellite cloud imagery.

More accurate initial conditions were obtained by using dynamic initialization compared to static initialization. The most accurate initialization and short-range model forecasts were produced by assimilating a combination of observed data over land and climatological information offshore during the 12-h preforecast period. Through the 24-h forecast period, errors in the coastal zone PBL depth and marine inversion strength were reduced by 65% and 41%, respectively, compared to the static-initialization control experiments. Without proper initialization of the offshore MBL, coastal zone forecasts degraded with time due to the long timescale of physical processes responsible for generating the MBL structure over cold, low-latitude oceans. Therefore, improvement of the model initial conditions in the California coastal zone by assimilation of climatological information offshore in combination with observed conditions near the coast proved to be an effective strategy for increasing short-range forecast accuracy.

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Michael J. Mueller
,
Bachir Annane
,
S. Mark Leidner
, and
Lidia Cucurull

Abstract

An observing system experiment was conducted to assess the impact of wind products derived from the Cyclone Global Navigation Satellite System (CYGNSS) on tropical cyclone track, maximum 10-m wind speed V max, and minimum sea level pressure forecasts. The experiment used a global data assimilation and forecast system, and the impact of both CYGNSS-derived scalar and vector wind retrievals was investigated. The CYGNSS-derived vector wind products were generated by optimally combining the scalar winds and a gridded a priori vector field. Additional tests investigated the impact of CYGNSS data on a regional model through the impact of lateral boundary and initial conditions from the global model during the developmental phase of Hurricane Michael (2018). In the global model, statistically significant track forecast improvements of 20–40 km were found in the first 60 h. The V max forecasts showed some significant degradations of ~2 kt at a few lead times, especially in the first 24 h. At most lead times, impacts were not statistically significant. Degradations in V max for Hurricane Michael in the global model were largely attributable to a failure of the CYGNSS-derived scalar wind test to produce rapid intensification in the forecast initialized at 0000 UTC 7 October. The storm in this test was notably less organized and symmetrical than in the control and CYGNSS-derived vector wind test. The regional model used initial and lateral boundary conditions from the global control and CYGNSS scalar wind tests. The regional forecasts showed large improvements in track, V max, and minimum sea level pressure.

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Christina Holt
,
Istvan Szunyogh
,
Gyorgyi Gyarmati
,
S. Mark Leidner
, and
Ross N. Hoffman

Abstract

The standard statistical model of data assimilation assumes that the background and observation errors are normally distributed, and the first- and second-order statistical moments of the two distributions are known or can be accurately estimated. Because these assumptions are never satisfied completely in practice, data assimilation schemes must be robust to errors in the underlying statistical model. This paper tests simple approaches to improving the robustness of data assimilation in tropical cyclone (TC) regions.

Analysis–forecast experiments are carried out with three types of data—Tropical Cyclone Vitals (TCVitals), DOTSTAR, and QuikSCAT—that are particularly relevant for TCs and with an ensemble-based data assimilation scheme that prepares a global analysis and a limited-area analysis in a TC basin simultaneously. The results of the experiments demonstrate that significant analysis and forecast improvements can be achieved for TCs that are category 1 and higher by improving the robustness of the data assimilation scheme.

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S. Mark Leidner
,
Thomas Nehrkorn
,
John Henderson
,
Marikate Mountain
,
Tom Yunck
, and
Ross N. Hoffman

Abstract

Global Navigation Satellite System (GNSS) radio occultations (RO) over the last 10 years have proved to be a valuable and essentially unbiased data source for operational global numerical weather prediction. However, the existing sampling coverage is too sparse in both space and time to support forecasting of severe mesoscale weather. In this study, the case study or quick observing system simulation experiment (QuickOSSE) framework is used to quantify the impact of vastly increased numbers of GNSS RO profiles on mesoscale weather analysis and forecasting. The current study focuses on a severe convective weather event that produced both a tornado and flash flooding in Oklahoma on 31 May 2013. The WRF Model is used to compute a realistic and faithful depiction of reality. This 2-km “nature run” (NR) serves as the “truth” in this study. The NR is sampled by two proposed constellations of GNSS RO receivers that would produce 250 thousand and 2.5 million profiles per day globally. These data are then assimilated using WRF and a 24-member, 18-km-resolution, physics-based ensemble Kalman filter. The data assimilation is cycled hourly and makes use of a nonlocal, excess phase observation operator for RO data. The assimilation of greatly increased numbers of RO profiles produces improved analyses, particularly of the lower-tropospheric moisture fields. The forecast results suggest positive impacts on convective initiation. Additional experiments should be conducted for different weather scenarios and with improved OSSE systems.

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Bachir Annane
,
Brian McNoldy
,
S. Mark Leidner
,
Ross Hoffman
,
Robert Atlas
, and
Sharanya J. Majumdar

Abstract

In preparation for the launch of the NASA Cyclone Global Navigation Satellite System (CYGNSS), a variety of observing system simulation experiments (OSSEs) were conducted to develop, tune, and assess methods of assimilating these novel observations of ocean surface winds. From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality. The OSSE system makes use of NOAA’s HWRF Model and GSI data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method (VAM). Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 h were tested, and the 3-h impacts were consistently best. One-day forecasts from CYGNSS VAM vector winds were the most dynamically consistent with the NR. The OSSEs have a number of limitations; the most noteworthy is that this is a case study, and static background error covariances were used.

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