Assimilating humidity pseudo‐observations derived from the cloud profiling radar aboard CloudSat in ALADIN 3D‐Var

Storto, Andrea; Tveter, Frank Thomas

doi:10.1002/met.144

Cited by 13 publications

(24 citation statements)

References 50 publications

(42 reference statements)

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“…HARMONIE is the name given to the ALADIN core-based limited-area model with the HIRLAM community-developed framework. It is not operational at the Norwegian Meteorological Institute, but it has been already used for observations impact studies [Storto and Tveter, 2009;Storto and Randriamampianina, 2010].…”

Section: Regional Modelmentioning

confidence: 99%

Ensemble variational assimilation for the representation of background error covariances in a high‐latitude regional model

Storto¹,

Randriamampianina²

2010

J. Geophys. Res.

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[1] Statistical objective analysis requires the explicit specification of the observation and background error covariances. This paper deals with the estimation of the latter within a high-latitude regional model. Four different approaches have been adopted to simulate the error evolution in the analysis and forecast steps of the model: i) the largely-adopted NMC method, applied to both a winter and a summer season data set, ii) global ensemble analyses projected forward to the 6-hour forecast range by the limited-area model itself, iii) limited-area ensemble variational assimilation with unperturbed lateral boundary conditions (LBCs) and iv) limited-area ensemble variational assimilation with perturbed lateral boundary conditions. The structure of the four background error covariance matrices was extensively compared. It turned out that the NMC-derived standard deviations had stronger amplitude at the large scales, especially in the lower troposphere, compared to those derived using the ensemble technique, and much broader horizontal and large-scale vertical correlations. The contribution of lateral boundary perturbations is shown to be significant. Moreover, neglecting these LBCs perturbations contributes to unrealistic features, such as artificial small variances near the boundaries, which tend to spuriously propagate towards the inner area. Furthermore, humidity variances and associated cross-covariances tend to be weaker in the ensemble assimilation experiments, in particular when lateral boundary conditions are not perturbed. A onemonth assimilation period allowed us to evaluate the impact of the different background error statistics on the forecasts: wind, geopotential and partly temperature are clearly benefiting from the use of ensemble-based background error covariances; humidity skill scores are noticeably improved when background error covariances from limited-area ensemble assimilation were used. The seasonality of the background error structures was also investigated, and it was found non-negligible for the performance of the data assimilation and forecast systems.Citation: Storto, A., and R. Randriamampianina (2010), Ensemble variational assimilation for the representation of background error covariances in a high-latitude regional model,

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Section: Regional Modelmentioning

confidence: 99%

Ensemble variational assimilation for the representation of background error covariances in a high‐latitude regional model

Storto¹,

Randriamampianina²

2010

J. Geophys. Res.

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“…Experimental assimilation of the Atmospheric Radiation Measurement programme (ARM) cloud radar observations combined with the ground-based precipitation measurements and GPS total column water-vapour retrievals has been proposed by Lopez et al (2006) using a 2D-Var technique (the second dimension being time). Storto and Tveter (2009) used an experimental procedure to assimilate the cloud fraction from CloudSat Cloud Profiling Radar (CPR) in a limited-area 3D-Var system through the use of humidity pseudoobservations derived from a one-dimensional Bayesian analysis.…”

Section: Introductionmentioning

confidence: 99%

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Janisková

Lopez

Bauer

2011

Quart J Royal Meteoro Soc

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Observations providing three-dimensional information on clouds from space-borne active instruments on board CloudSat and CALIPSO are already available and new satellites, such as EarthCARE, should appear in the near future. This opens new possibilities for exploring the usefulness of this kind of observation, not only for improving model parametrizations but also for investigating their usage in data assimilation to extract information from the data so as to improve the initial atmospheric state. In this study, a 1D + 4D-Var technique has been selected to study the impact of observations related to clouds on 4D-Var analyses and subsequent forecasts. Using this two-step approach, temperature and specific humidity profiles retrieved from 1D-Var assimilation of CloudSat observations have been included in the 4D-Var system. Several experiments have been run for a couple of selected meteorological situations.

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“…The AMMA campaign has shown that improvements in the upper-air observation network are possible and can in some cases be relatively inexpensive ), but sufficient funds and staff are required. The shortage of radiosonde data makes effective use of satellite data particularly important: the use of novel techniques such as those discussed in Storto and Tveter (2009) and Kostka et al (2014) are of particular interest as there is potential for improved representation of subgrid-scale processes in (re)analyses for a relatively small investment.…”

Section: Discussionmentioning

confidence: 99%

“…One possible avenue is by the assimilation of clouds into three-/four-dimensional variational data assimilation (3D/4DVAR) systems. Storto and Tveter (2009) illustrate how a pseudoobservation assimilation scheme using CloudSat data has a clear benefit on a number of different fields in a 3DVAR assimilation scheme. They also suggest that the technique might be extended to be used for other sources of data including satellite-borne infrared sensors.…”

Section: ) Behavior Of the Zonal-mean Itdmentioning

confidence: 99%

Disagreements in Low-Level Moisture between (Re)Analyses over Summertime West Africa

Roberts

Marsham

Knippertz

2015

Monthly Weather Review

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Reanalysis and operational analysis products are routinely used as the best estimates of the atmospheric state for operational and research purposes. However, different models, assimilation techniques, and assimilated datasets lead to differences between products. Here, such differences in the distribution of low-level water vapor over summertime West Africa are analyzed, as reflected in the zonal mean position of the leading edge of the West African monsoon [the intertropical discontinuity (ITD)] using five reanalyses [NCEP-NCAR, NCEP-Department of Energy (DOE), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), the Climate Forecast System Reanalysis (CFSR), and the Interim ECMWF ReAnalysis (ERA-Interim)] and two operational analyses [Global Forecast System (GFS) and ECMWF] during the 11 monsoon seasons (April-September) from 2000 to 2010. Specific humidity differences regularly reach 50% of the mean value over areas spanning hundreds of kilometers and often coincide with northward excursions of the ITD that last several days and bring unusual rainfall to the Sahel and Sahara. The largest disagreements occur during the southward retreat of the ITD and are connected with anomalously high values of aerosol optical depth, consistent with the production of haboob dust storms. The results suggest that known errors in the representation of moist convection and cold pools may contribute to the identified disagreements. A large reduction in disagreement occurs in 2006, when upper-air observations were enhanced during the African Monsoon Multidisciplinary Analysis (AMMA) campaign, pointing to an insufficient observational constraint of the (re)analyses in other years. It is hoped that this work will raise awareness of the limited reliability of (re)analysis products over West Africa during the summer, particularly during northward surges of the ITD, and will instigate further work to improve their quality.

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Assimilating humidity pseudo‐observations derived from the cloud profiling radar aboard CloudSat in ALADIN 3D‐Var

Cited by 13 publications

References 50 publications

Ensemble variational assimilation for the representation of background error covariances in a high‐latitude regional model

Ensemble variational assimilation for the representation of background error covariances in a high‐latitude regional model

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Disagreements in Low-Level Moisture between (Re)Analyses over Summertime West Africa

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