Assimilating Surface Data to Improve the Accuracy of Atmospheric Boundary Layer Simulations

Alapaty, Kiran; Seaman, Nelson L.; Niyogi, D.; Hanna, Adel

doi:10.1175/1520-0450(2001)040<2068:asdtit>2.0.co;2

Cited by 40 publications

(41 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these cases, the observations were first analyzed on the model's grid and then assimilated. The same results were obtained by Alapaty et al (2001) using an idealized 1-D model of PBL. Stauffer and Seaman (1994) compared the use of large-scale analysis-nudging and fine-scale observationnudging techniques alone or in combination, during two case studies.…”

supporting

confidence: 79%

Screen-level non-GTS data assimilation in a limited-area mesoscale model

Milelli¹,

Turco

Oberto³

2010

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The forecast in areas of very complex topography, as for instance the Alpine region, is still a challenge even for the new generation of numerical weather prediction models which aim at reaching the km-scale. The problem is enhanced by a general lack of standard observations, which is even more evident over the southern side of the Alps. For this reason, it would be useful to increase the performance of the mathematical models by locally assimilating non-conventional data. Since in ARPA Piemonte there is the availability of a great number of non-GTS stations, it has been decided to assimilate the 2 m temperature, coming from this dataset, in the very-high resolution version of the COSMO model, which has a horizontal resolution of about 3 km, more similar to the average resolution of the thermometers. Four different weather situations have been considered, ranging from spring to winter, from cloudy to clear sky. The aim of the work is to investigate the effects of the assimilation of non-GTS data in order to create an operational very high-resolution analysis, but also to test the option of running in the future a very short-range forecast starting from these analyses (RUC or Rapid Update Cycle). The results, in terms of Root Mean Square Error, Mean Error and diurnal cycle of some surface variables such as 2 m temperature, 2 m relative humidity and 10 m wind intensity show a positive impact during the assimilation cycle which tends to dissipate a few hours after the end of it. Moreover, the 2 m temperature assimilation has a slightly positive or neutral impact on the vertical profiles of temperature, eventhough some calibration is needed for the precipitation field which is too much perturbed during the assimilation cycle, while it is unaffected in the forecast period. So the stability of the planetary boundary layer, on the one hand, has not been particu- Milelli (m.milelli@arpa.piemonte.it) larly improved by the new-data assimilation, but, on the other hand, it has not been destroyed. It has to be pointed out that a correct description of the planetary boundary layer, even only the lowest part of it, could be helpful to the forecasters and, in general, to the users, in order to deal with meteorological hazards such as snow (in particular snow/rain limit definition), or fog (description of temperature inversions).

show abstract

supporting

confidence: 79%

Screen-level non-GTS data assimilation in a limited-area mesoscale model

Milelli¹,

Turco

Oberto³

2010

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Castelli et al (1999) used FIFE radiometric surface temperature in an adjoint assimilation method to derive soil wetness and surface heat fluxes. Alapaty et al (2001) used near-surface meteorology assimilated into a 1-D planetary boundary layer model, to adjust the turbulent profile of the atmosphere, and provide better surface fluxes. Likewise, Margulis and Entekhabi (2003) used FIFE near-surface air temperature moisture and radiometric surface temperature in a variational assimilation approach for atmospheric boundary layer flux assimilation.…”

Section: Introductionmentioning

confidence: 99%

Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

Bosilovich

Radakovich

Silva

et al. 2007

Journal of the Meteorological Society of Japan

View full text Add to dashboard Cite

In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, a incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 W m À2 . At the Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.

show abstract

“…Given this complexity, some modelers have resorted to simpler models constrained by satellite observations to recover fluxes as a residual. In particular, McNider et al [1994], Jones et al [1998], Alapaty et al [2001], and others have pro posed and used morning satellite surface tendencies to infer the moisture availability and evening tendencies to infer heat capac ity [McNider et al, 2005]. The triangle method of Gillies et al [1997] is another example where a look-up table approach is used to derive surface energy fluxes from satellite observed values of vegetation frac tion and surface radiant temperature.…”

Section: Methodsmentioning

confidence: 99%

Satellite‐based model parameterization of diabatic heating

et al. 2007

View full text Add to dashboard Cite

Future meteorological satellites are expected to provide much needed fine‐scale information that can improve the accuracy of weather and climate models. As one application of this improved capability, we introduce the concept of a generalized parameterization framework using satellite datasets that will increase the accuracy and the computational efficiency of weather and climate modeling. In an atmospheric model, several different parameterizations usually are used to reproduce the various physical processes. However, it is generally unrealistic to separate the processes in this artificial way since the observations and physics make no such artificial separation. Thus, we propose a new unified parameterization framework to remove the unrealistic separation between parameterizations.

show abstract

Assimilating Surface Data to Improve the Accuracy of Atmospheric Boundary Layer Simulations

Cited by 40 publications

References 26 publications

Screen-level non-GTS data assimilation in a limited-area mesoscale model

Screen-level non-GTS data assimilation in a limited-area mesoscale model

Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

Satellite‐based model parameterization of diabatic heating

Contact Info

Product

Resources

About