Assimilation of water vapor sensitive infrared brightness temperature observations during a high impact weather event

Otkin, Jason A.

doi:10.1029/2012jd017568

Cited by 50 publications

(41 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Elsewhere, much progress is being made (e.g. Vukicevic et al, 2006;Otkin, 2012;Martinet et al, 2013;Stengel et al, 2013;Harnisch et al, 2016) but nothing is yet operational. One example of the difficulties is that the allsky observation-error model will have to simultaneously model inter-channel error correlations, which have proved important in clear-sky infrared assimilation (e.g.…”

Section: More Observationsmentioning

confidence: 99%

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

Geer

Baordo

Bormann

et al. 2017

Quart J Royal Meteoro Soc

160

174

View full text Add to dashboard Cite

Ten years ago, humidity observations were thought to give little benefit to global weather forecasts. Nowadays, at the European Centre for Medium‐range Weather Forecasts, satellite microwave radiances sensitive to humidity, cloud and precipitation provide 20% of short‐range forecast impact, as measured by adjoint‐based forecast sensitivity diagnostics. This makes them one of the most important sources of data and equivalent in impact to microwave temperature sounding observations. Forecasts of dynamical quantities, and precipitation, are improved out to at least day 6. This article reviews the impact of and the science behind these data. It is not straightforward to assimilate cloud and precipitation‐affected observations when the intrinsic predictability of cloud and precipitation features is limited. Assimilation systems must be able to operate in the presence of all‐pervasive cloud and precipitation ‘mislocation’ errors. However, by assimilating these observations using the ‘all‐sky’ approach, and supported by advances in data assimilation and forecast modelling, modern data assimilation systems can infer the dynamical state of the atmosphere, not just from traditional temperature‐related observations, but from observations of humidity, cloud and precipitation.

show abstract

Section: More Observationsmentioning

confidence: 99%

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

Geer

Baordo

Bormann

et al. 2017

Quart J Royal Meteoro Soc

160

174

View full text Add to dashboard Cite

show abstract

“…Both ABI and AHI contain 2 visible, 4 near‐infrared, and 10 infrared channels. The peaks of the clear‐sky vertical weighting functions for the brightness temperatures from channels 8–10 fall in the troposphere, indicating that they are sensitive to water vapor in the upper, middle, and lower troposphere [e.g., Otkin , , Figure 1]. The brightness temperatures from these three channels are the focus of this first proof‐of‐concept study.…”

Section: Introductionmentioning

confidence: 99%

Potential impacts of assimilating all‐sky infrared satellite radiances from GOES‐R on convection‐permitting analysis and prediction of tropical cyclones

Zhang

Minamide

Clothiaux

2016

Geophysical Research Letters

130

168

View full text Add to dashboard Cite

The potential impacts of GOES‐R satellite radiances on tropical cyclone analysis and prediction were examined through ensemble correlations between simulated infrared brightness temperatures and various model state variables. The impacts of assimilating GOES‐R all‐sky infrared brightness temperatures on tropical cyclone analysis and prediction were further demonstrated through a series of convection‐permitting observing system simulation experiments using an ensemble Kalman filter under both perfect and imperfect model scenarios. Assimilation of the high temporal and spatial resolution infrared radiances not only constrained well the thermodynamic variables, including temperature, moisture, and hydrometeors, but also considerably reduced analysis and forecast errors in the wind fields. The potential of all‐sky radiances is further demonstrated through an additional proof‐of‐concept experiment assimilating real‐data infrared brightness temperatures from GOES 13 satellite which was operational in an enhanced scanning mode during Hurricane Karl (2010).

show abstract

“…In recent years the main focus has been on the assimilation of radiances from sounding instruments on polar‐orbiting satellites such as the Advanced Microwave Sounding Unit (AMSU), High Resolution Infrared Sounder (HIRS), the Atmospheric Infrared Sounder (AIRS), or the Infrared Atmospheric Sounding Interferometer (IASI). More recently, efforts have been made to assimilate radiances from geostationary satellites, such as the Visible and InfraRed Imager (MVIRI) on board Meteosat‐7 (Köpken et al ., ), the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board Meteosat‐8 (Szyndel et al ., ; Stengel et al ., ), the Very High Resolution Radiometer (VHRR) on board the Kalpana‐1 satellite (Singh et al ., ), the Geostationary Operational Environmental Satellite (GOES) imager (Zou et al ., ), the GOES‐R simulated radiances (Otkin, ; Jones et al ., ) and the Multi‐functional Transport Satellites (MTSAT) infrared imager (Okamoto, ). These studies showed a neutral or slightly positive impact of radiances from geostationary satellites on forecast skill.…”

Section: Introductionmentioning

confidence: 99%

Impact of the assimilation of INSAT‐3D radiances on short‐range weather forecasts

Singh

Ojha

Kishtawal

et al. 2015

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

The present study concentrates on the assimilation of clear‐sky radiances from the recently launched INSAT‐3D satellite. The imager and sounder are two primary meteorological instruments aboard the INSAT‐3D satellite. Pre‐assimilation monitoring of the radiances has been carried out from April to July 2014. A double‐difference technique using the High Resolution Infrared Sounder (HIRS) on MetOp‐A is employed to remove the effect of deficiencies in the model‐analysed profiles which can contribute to the biases. Observed radiances of both instruments appear to be colder (∼1.5–2 K) with respect to radiative transfer model (RTM) simulated radiances, except water vapour channel radiances, which are warmer (0.5–1 K). The standard deviations of observed minus RTM‐simulated radiances are between 0.5 and 1.5 K. Prior to data assimilation, these biases are corrected using a variational bias correction scheme. The largest impacts on the analyses, when assimilating imager radiances, are found in the mid‐ and upper‐tropospheric moisture, while assimilation of sounder radiances impacted both moisture and temperature throughout the troposphere. The more accurate analyses with the INSAT‐3D radiance assimilation lead to improved moisture, wind, temperature and precipitation forecasts compared to the control case in which only conventional observations were assimilated. The results demonstrate the ability of temperature‐ and water vapour‐sensitive radiances to improve not only the temperature and moisture fields, but also the wind fields, enhancing their importance, particularly over tropical regions where wind observations are more important.

show abstract

Assimilation of water vapor sensitive infrared brightness temperature observations during a high impact weather event

Cited by 50 publications

References 77 publications

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

The growing impact of satellite observations sensitive to humidity, cloud and precipitation

Potential impacts of assimilating all‐sky infrared satellite radiances from GOES‐R on convection‐permitting analysis and prediction of tropical cyclones

Impact of the assimilation of INSAT‐3D radiances on short‐range weather forecasts

Contact Info

Product

Resources

About