Recent Innovations in Deriving Tropospheric Winds from Meteorological Satellites

Velden, Christopher S.; Daniels, Jaime; Stettner, David; Santek, David; Key, Jeff; Dunion, Jason; Holmlund, Kenneth; Dengel, Gail A.; Bresky, Wayne; Menzel, W. Paul

doi:10.1175/bams-86-2-205

Cited by 182 publications

(114 citation statements)

References 35 publications

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“…1 of Schwartz et al (2015) for the exact dimensions]. Observations from rawinsondes, METAR, marine, aircraft meteorological data reports (AMDAR), atmospheric motion vectors (AMV; Velden et al 2005), and Global Positioning System radio occultation observations (Kursinski et al 1997) are assimilated using the Data Assimilation Research Testbed (DART; Anderson et al 2009), which is an implementation of the ensemble adjustment Kalman filter (EAKF; Anderson 2001). The interested reader is directed to Table 3 of Romine et al (2013) for a list of observation types that were assimilated from each platform and the observation error sources.…”

Section: Forecast Descriptionmentioning

confidence: 99%

Sensitivity of Dryline Convection Forecasts to Upstream Forecast Errors for Two Weakly Forced MPEX Cases

Torn

Romine

Galarneau

2017

Monthly Weather Review

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The sensitivity of convective forecasts along the Texas dryline to upstream forecast fields at earlier lead times is evaluated for two consecutive days (27-28 May) characterized by no clear synoptic forcing for convection initiation (CI) during the 2013 Mesoscale Predictability Experiment (MPEX) by applying the ensemble-based sensitivity technique to convection-allowing WRF ensemble forecasts. For both cases, the members with stronger convection are characterized by higher water vapor just above the top of the boundary layer, which is associated with lower convective inhibition (CIN) at the time of CI. Forecast convection is sensitive to the lowertropospheric water vapor and zonal wind at earlier lead times farther south along the dryline, such that increasing the water vapor and/or making the wind more easterly is associated with more convection. For 28 May, the water vapor along the dryline is also sensitive to the convection that occurs around 0600 UTC, which leads to cold pool-induced surface divergence that subsequently shifts the dryline north or south. Ensemble members that correctly have decreased convection in the Texas Panhandle on 28 May have more accurate forecasts of water vapor and meridional wind with respect to dropwindsondes in the sensitive region 9 h prior to CI compared to members with more extensive convection. Reducing the 0-h water vapor within the sensitive region can suppress convection in members with extensive convection; however, increasing the 0-h water vapor does not lead to more convection in members without convection.

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Section: Forecast Descriptionmentioning

confidence: 99%

Sensitivity of Dryline Convection Forecasts to Upstream Forecast Errors for Two Weakly Forced MPEX Cases

Torn

Romine

Galarneau

2017

Monthly Weather Review

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“…When MTSAT-1R replaced GOES-9 in 2005, methods similar to those employed at NOAA (Daniels et al 2000, Velden et al 2005) and also at the Bureau (Le Marshall et al 2000b) were used to generate AMVs from the MTSAT-1R HIRID data received at the Crib Point groundstation, in Victoria. Three sequential images from MTSAT-1R were navigated using land features to ensure that there was consistency between images used for estimating cloud displacement.…”

Section: Mtsat-1r Atmospheric Motion Vectorsmentioning

confidence: 99%

The generation and assimilation of continuous AMVs with 4DVar

Marshall¹,

Seecamp²,

Xiao³

et al. 2011

AMOJ

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“…The motion of mesoscale convective activity is a natural source for velocimetry. Indeed, there exist products that deduce "winds" by estimating the motion of temperature, vapor and other fields evolving in time [7,8]. In this paper, we present an algorithm for velocimetry from observed motion from satellite observations such as GOES, AMSU, TRMM, or radar data such as NOWRAD.…”

Section: Application To Velocimetrymentioning

confidence: 99%

“…To compare, we use CIMSS wind-data satellite data [8], depicted in Figure 4 obtained from CIMSS analysis on 2006-06-04 at 09Z. CIMSS wind-data is shown over the US great plains, and were obtained from the sounder.…”

Section: Application To Velocimetrymentioning

confidence: 99%

Two Extensions of Data Assimilation by Field Alignment

Ravela

2007

Computational Science – ICCS 2007

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Abstract. Classical formulations of data-assimilation perform poorly when forecast locations of weather systems are displaced from their observations. They compensate position errors by adjusting amplitudes, which can produce unacceptably "distorted" states. Motivated by cyclones, in earlier work we show a new method for handling position and amplitude errors using a single variational objective. The solution could be used with either ensemble or deterministic methods. In this paper, extension of this work in two directions is reported. First, the methodology is extended to multivariate fields commonly used in models, thus making this method readily applicable. Second, an application of this methodology to rainfall modeling is presented.

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Recent Innovations in Deriving Tropospheric Winds from Meteorological Satellites

Cited by 182 publications

References 35 publications

Sensitivity of Dryline Convection Forecasts to Upstream Forecast Errors for Two Weakly Forced MPEX Cases

Sensitivity of Dryline Convection Forecasts to Upstream Forecast Errors for Two Weakly Forced MPEX Cases

The generation and assimilation of continuous AMVs with 4DVar

Two Extensions of Data Assimilation by Field Alignment

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