All-Sky Microwave Radiance Assimilation in NCEP’s GSI Analysis System

Zhu, Yutong; Liu, Emily; Mahajan, Rahul; Thomas, Catherine; Groff, David; Delst, Paul van; Collard, Andrew; Kleist, Daryl; Treadon, Russ; Derber, John

doi:10.1175/mwr-d-15-0445.1

Cited by 97 publications

(106 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Radiance observations affected by cloud or precipitation have potentially more useful information than clear‐sky radiance observations. Currently, at most NWP centers, various forms of all‐sky radiance observations are assimilated [ Bauer et al ., ; Kazumori et al ., ; Zhu et al ., ], but all‐sky radiance assimilation has some limitations because of its complexity.…”

Section: Introductionmentioning

confidence: 79%

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

Choi

Cha

Lee

et al. 2017

J Adv Model Earth Syst

View full text Add to dashboard Cite

A total of three binary tropical cyclone (TC) cases over the Western North Pacific are selected to investigate the effects of satellite radiance data assimilation on analyses and forecasts of binary TCs. Two parallel cycling experiments with a 6 h interval are performed for each binary TC case, and the difference between the two experiments is whether satellite radiance observations are assimilated. Satellite radiance observations are assimilated using the Weather Research and Forecasting Data Assimilation (WRFDA)'s three‐dimensional variational (3D‐Var) system, which includes the observation operator, quality control procedures, and bias correction algorithm for radiance observations. On average, radiance assimilation results in slight improvements of environmental fields and track forecasts of binary TC cases, but the detailed effects vary with the case. When there is no direct interaction between binary TCs, radiance assimilation leads to better depictions of environmental fields, and finally it results in improved track forecasts. However, positive effects of radiance assimilation on track forecasts can be reduced when there exists a direct interaction between binary TCs and intensities/structures of binary TCs are not represented well. An initialization method (e.g., dynamic initialization) combined with radiance assimilation and/or more advanced DA techniques (e.g., hybrid method) can be considered to overcome these limitations.

show abstract

Section: Introductionmentioning

confidence: 79%

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

Choi

Cha

Lee

et al. 2017

J Adv Model Earth Syst

View full text Add to dashboard Cite

show abstract

“…We note here that, by building on the above studies, it has been demonstrated that the direct assimilation of all‐sky MW and IR satellite radiances is now possible in principle. All‐sky MW data are considered easier to use than all‐sky IR data due to the smoother properties of the MW radiative transfer model output between cloudy and clear scenes (Bauer et al ., ), so these data were the first to be treated directly (Bauer et al ., ; Zhu et al ., ; Migliorini and Candy, ). Recently though, satellite IR data have also been assimilated directly (Zhang et al ., ; Minamide and Zhang, ; Okamoto et al ., ), which is discussed further in Sections 6.5, 6.8 and 6.9.…”

Section: Methods Of Assimilation Of Atmospheric Water Informationmentioning

confidence: 99%

“…More recently there have been successful studies showing how IR and MW satellite data can be assimilated in all-sky conditions using a number of direct assimilation methods. Studies include those of Minamide and Zhang (2018) and Okamoto et al (2019) for IR data, and Zhu et al (2016) and Migliorini and Candy (2019) for MW data. All of these studies have used some kind of observation error variance inflation scheme to account for errors due to the presence of cloud.…”

Section: Sensitivities Of Radiances To Cloud and Precipitation Fieldsmentioning

confidence: 99%

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

Bannister

Chipilski

Martínez‐Alvarado

2019

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

While contemporary numerical weather prediction models represent the large‐scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts of small‐scale features such as convective rainfall. Even though high‐resolution models resolve more of the flow, and are therefore arguably more accurate, moist convective flow becomes increasingly nonlinear and dynamically unstable. Importantly, the models' initial conditions are typically sub‐optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. To address issues regarding the use of atmospheric water‐related observations – especially at convective scales (also known as storm scales) – this article discusses the observation and assimilation of water‐related quantities. Special emphasis is placed on background error statistics for variational and hybrid methods which need special attention for water variables. The challenges of convective‐scale data assimilation of atmospheric water information are discussed, which are more difficult to tackle than at larger scales. Some of the most important challenges include the greater degree of inhomogeneity and lower degree of smoothness of the flow, the high volume of water‐related observations (e.g. from radar, microwave and infrared instruments), the need to analyse a range of hydrometeors, the increasing importance of position errors in forecasts, the greater sophistication of forward models to allow use of indirect observations (e.g. cloud‐ and precipitation‐affected observations), the need to account for the flow‐dependent multivariate “balance” between atmospheric water and both dynamical and mass fields, and the inherent non‐Gaussian nature of atmospheric water variables.

show abstract

“…Satellite information has become one of the dominant sources for improving numerical weather prediction (NWP) model‐based forecasts. In the last few years, all‐sky satellite‐based microwave sounder data have shown great benefit to forecast improvement at operational NWP systems (Bauer et al, ; Geer et al, ; Zhu et al, ) and in the research community (Li et al, ; Yang et al, ; Zhang et al, ). Infrared (IR) radiances, however, are often not fed into the assimilation system because a large percentage of those data are contaminated with clouds (Eresmaa, ; Wang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Impacts of Observation Errors on Hurricane Forecasts When Assimilating Hyperspectral Infrared Sounder Radiances in Partially Cloudy Skies

Wang

et al. 2019

JGR Atmospheres

View full text Add to dashboard Cite

Hyperspectral infrared (IR) sounders provide high vertical resolution atmospheric sounding information that can improve the forecast accuracy of numerical weather prediction (NWP) models. Due to the challenges of assimilating cloudy radiances, NWP centers usually assimilate only radiances that are not affected by clouds. An imager based cloud‐clearing technique provides an alternative and effective way to remove the cloud effects from a partially cloudy field‐of‐view and derive the equivalent clear sky radiances or the cloud‐cleared radiances (CCRs) for assimilation in NWP. Since the observation error is amplified in the cloud‐clearing, or noise amplification process, it is necessary to inflate the observation errors appropriately in order to achieve the optimal value‐added impact from assimilating CCRs. The estimation of observation error inflation is established and discussed. Hurricane Harvey (2017) and Hurricane Maria (2017) are used to simulate and understand the impacts of observation error inflation on the assimilation of Cross‐track Infrared Sounder CCRs for hurricane forecast improvement. Both the precipitation location and intensity forecasts are improved when assimilating CCRs with an inflated observation error for Hurricane Harvey (2017). Assimilating CCRs with an inflated observation error adjusts the temperature and geopotential height fields and further affects the hurricane structures to improve the hurricane track forecasts, thereby demonstrating the importance of using hyperspectral IR measurements in partially cloudy skies for simulating the hurricane structure and improving its forecast. This method can be applied to other imager/sounder combined observations for improving sounder radiance assimilation in cloudy skies and has potential for operational applications.

show abstract

All-Sky Microwave Radiance Assimilation in NCEP’s GSI Analysis System

Cited by 97 publications

References 40 publications

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

Impacts of Observation Errors on Hurricane Forecasts When Assimilating Hyperspectral Infrared Sounder Radiances in Partially Cloudy Skies

Contact Info

Product

Resources

About