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Ross N. Hoffman
and
S. Mark Leidner

Abstract

The size of errors due to linear time interpolation varies parabolically with a maximum at the center of the interpolation interval in most of the cases examined here. These cases include simple situations that are analyzed analytically and mesoscale model simulations of the ocean surface wind that are analyzed empirically.

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Ross N. Hoffman
and
S. Mark Leidner

Abstract

The NASA Quick Scatterometer (QuikSCAT) satellite carries the SeaWinds instrument, the first satellite-borne scanning radar scatterometer. QuikSCAT, which was launched on 19 June 1999, is designed to provide accurate ocean surface winds in all conditions except for moderate to heavy rain (i.e., except for vertically integrated rain rate >2.0 km mm h−1, the value used to tune the SeaWinds rain flag). QuikSCAT data are invaluable in providing high-quality, high-resolution winds to detect and locate precisely significant meteorological features and to produce accurate ocean surface wind analyses. QuikSCAT has an 1800-km-wide swath. A representative swath of data in the North Atlantic at 2200 UTC 28 September 2000, which contains several interesting features, reveals some of the capabilities of QuikSCAT. Careful quality control is vital for flagging data that are affected by rain and for flagging errors during ambiguity removal. In addition, an understanding of the instrument and algorithm characteristics provides insights into the factors controlling data quality for QuikSCAT. For example data quality is reduced for low wind speeds, and for locations either close to nadir or to the swath edges. The special data characteristics of the QuikSCAT scatterometer are revealed by examining the likelihood or objective function. The objective function is equal to the sum of squared scaled differences between observed and simulated normalized reflected radar power. The authors present typical examples and discuss the associated data quality concerns for different parts of the swath, for different wind speeds, and for rain versus no rain.

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

Abstract

A positive impact of adding directional information to observations from the Cyclone Global Navigation Satellite System (CYNGSS) constellation of microsatellites is observed in simulation using a high-resolution nature run of an Atlantic hurricane for a 4-day period. Directional information is added using a two-dimensional variational analysis method (VAM) for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The resulting wind vectors at CYGNSS data locations are more geophysically self-consistent when using high-resolution 6-h forecast backgrounds from a Hurricane Weather Research and Forecast Model (HWRF) control observing system simulation experiment (OSSE) compared to low-resolution 6-h forecasts from an associated Global Forecast System (GFS) model control OSSE. An important contributing factor is the large displacement error in the center of circulation in the GFS background wind fields that produces asymmetric circulations in the associated VAM analyses. Results of a limited OSSE indicate that CYGNSS winds reduce forecast error in hurricane intensity in 0–48-h forecasts compared to using no CYGNSS data. Assimilation of VAM-CYGNSS vector winds reduces maximum wind speed error by 2–5 kt (1 kt = 0.51 m s−1) and reduces minimum central pressure error by 2–5 hPa. The improvement in forecast intensity is notably larger and more consistent than the reduction in track error. The assimilation of VAM-CYGNSS wind vectors constrains analyses of surface wind field structures during OSSE more effectively than wind speeds alone. Because of incomplete sampling and the limitations of the data assimilation system used, CYGNSS scalar winds produce unwanted wind/pressure imbalances and asymmetries more often than the assimilation of VAM-CYGNSS data.

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Ross N. Hoffman
,
Christopher Grassotti
, and
S. Mark Leidner

Abstract

To examine the accuracy of the SeaWinds scatterometer wind data and rain flags, and how this accuracy depends on ground-based radar-estimated rain rate, SeaWinds data, WSI NEXRAD precipitation rates, and selected Eta analysis variables are collocated. [SeaWinds is the NASA scatterometer on the QuikSCAT and Advanced Earth Observing Satellite (ADEOS)-2 satellites, WSI NEXRAD precipitation data are from a Weather Services International Corporation product based on the U.S. Next Generation Radar (NEXRAD) network of Weather Surveillance Radar-1988 Doppler (WSR-88D) installations, and Eta is the NCEP operational mesoscale model.] Only data close to the east coast of the United States are collected, where both the WSI NEXRAD data and the Eta analyses are accurate.

For the subset of data for which WSI NEXRAD detects no rain, within the optimal part of the swath, and for Eta analysis wind speeds between 3 and 20 m s−1, the rms differences between SeaWinds and Eta analysis wind speed and direction are 1.73 m s−1 and 21°, respectively. These rms differences increase significantly whenever WSI NEXRAD detects rain, even light rain. The SeaWinds rain indices are strongly correlated with the WSI NEXRAD precipitation rates. While for high rain rates most winds are correctly flagged, many cases of light rain are not detected.

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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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Christopher Grassotti
,
S. Mark Leidner
,
Jean-François Louis
, and
Ross N. Hoffman

Abstract

The authors report on characteristics of a rain flag derived from collocation of visible and infrared image data with rain rates over the North Atlantic Ocean obtained from microwave imagery (SSM/I) during a 3-week period (15 October 1996–2 November 1996). The rain flag has been developed as part of an effort to provide an indication of contamination by heavy rainfall in NASA scatterometer datasets. The primary results of this analysis indicate 1) that a simple albedo/infrared brightness temperature threshold is capable of flagging most of the heavy rainfall, though with a fairly high rate of false alarms, and 2) that the small difference in optimal threshold between the Tropics and midlatitudes can probably be ignored. Use of the rain flag in 12 assimilation experiments during this period showed that the number of rain-flagged wind vector cells is generally less than 1% of the number of cells. Overall, the impact from using the rain-flagged data is generally less than 5 m s−1 and localized (less than 5° of latitude and longitude). However, in some cases, the effect of excluding just one to five rain-flagged points can change the resulting analysis significantly, because their placement is critical for defining the flow along a front or some other shear-dominated environment.

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Ross N. Hoffman
,
Joseph V. Ardizzone
,
S. Mark Leidner
,
Deborah K. Smith
, and
Robert Atlas

Abstract

The Desroziers diagnostics (DD) are applied to the cross-calibrated, multiplatform (CCMP) ocean surface wind datasets to estimate wind speed errors of the ECMWF background, the microwave satellite observations, and the resulting CCMP analysis. The DD confirm that the ECMWF operational surface wind speed error standard deviations vary with latitude in the range 0.8–1.3 m s−1 and that the cross-calibrated Remote Sensing Systems (RSS) wind speed retrievals’ standard deviations are in the range 0.5–0.7 m s−1. Further, the estimated CCMP analysis wind speed standard deviations are in the range 0.2–0.3 m s−1. The results suggest the need to revise the parameterization of the errors of the first guess at appropriate time (FGAT) procedure. Errors for wind speeds <16 m s−1 are homogeneous; however, for the relatively rare but critical higher wind speed situations, errors are much larger.

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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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