Stefano Migliorini

On the Equivalence between Radiance and Retrieval Assimilation

The need for consistent assimilation of satellite measurements for numerical weather prediction led operational meteorological centers to assimilate satellite radiances directly using variational data assimilation systems. More recently there has been a renewed interest in assimilating satellite retrievals (e.g., to avoid the use of relatively complicated radiative transfer models as observation operators for data assimilation). The aim of this paper is to provide a rigorous and comprehensive discussion of the conditions for the equivalence between radiance and retrieval assimilation. It is shown that two requirements need to be satisfied for the equivalence: (i) the radiance observation operator needs to be approximately linear in a region of the state space centered at the retrieval and with a radius of the order of the retrieval error; and (ii) any prior information used to constrain the retrieval should not underrepresent the variability of the state, so as to retain the information content of the measurements. Both these requirements can be tested in practice. When these requirements are met, retrievals can be transformed so as to represent only the portion of the state that is well constrained by the original radiance measurements and can be assimilated in a consistentand optimal way, by means of an appropriate observation operator and a unit matrix as error covariance. Finally, specific cases when retrieval assimilation can be more advantageous (e.g., when the estimate sought by the operational assimilation system depends on the first guess) are discussed.

Ensemble prediction for nowcasting with a convection-permitting model—I: description of the system and the impact of radar-derived surface precipitation rates

A key strategy to improve the skill of quantitative predictions of precipitation, as well as hazardous weather such as severe thunderstorms and flash floods is to exploit the use of observations of convective activity (e.g. from radar). In this paper, a convection-permitting ensemble prediction system (EPS) aimed at addressing the problems of forecasting localized weather events with relatively short predictability time scale and based on a 1.5 km grid-length version of the Met Office Unified Model is presented. Particular attention is given to the impact of using predicted observations of radar-derived precipitation intensity in the ensemble transform Kalman filter (ETKF) used within the EPS. Our initial results based on the use of a 24-member ensemble of forecasts for two summer case studies show that the convectivescale EPS produces fairly reliable forecasts of temperature, horizontal winds and relative humidity at 1 h lead time, as evident from the inspection of rank histograms. On the other hand, the rank histograms seem also to show that the EPS generates too much spread for forecasts of (i) surface pressure and (ii) surface precipitation intensity. These may indicate that for (i) the value of surface pressure observation error standard deviation used to generate surface pressure rank histograms is too large and for (ii) may be the result of non-Gaussian precipitation observation errors. However, further investigations are needed to better understand these findings. Finally, the inclusion of predicted observations of precipitation from radar in the 24-member EPS considered in this paper does not seem to improve the 1-h lead time forecast skill.

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

With movement toward kilometer-scale ensembles, new techniques are needed for their characterization. A new methodology is presented for detailed spatial ensemble characterization using the fractions skill score (FSS). To evaluate spatial forecast differences, the average and standard deviation are taken of the FSS calculated over all ensemble member‐member pairs at different scales and lead times. These methods were found to give important information about the ensemble behavior allowing the identification of useful spatial scales,spinuptimesforthemodel,andupscalegrowthoferrorsandforecastdifferences.Theensemblespread was found to be highly dependent on the spatial scales considered and the threshold applied to the field. High thresholds picked out localized and intense values that gave large temporal variability in ensemble spread: local processes and undersampling dominate for these thresholds. For lower thresholds the ensemble spread increases with time as differences between the ensemble members upscale. Two convective cases were investigated based on the Met Office United Model run at 2.2-km resolution. Different ensemble types were considered: ensembles produced using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) and an ensemble produced using different model physics configurations. Comparison of the MOGREPS and multiphysics ensembles demonstrated the utility of spatial ensemble evaluation techniques for assessing the impact of different perturbation strategies and the need for assessing spread at different, believable, spatial scales.

Use of the Information Content in Satellite Measurements for an Efficient Interface to Data Assimilation

Abstract Satellite observations are the most assimilated data type by operational meteorological centers. Spaceborne instruments can make measurements all over the globe and provide observations for assimilation even where the coverage of other data is poor. It is therefore most important that such observations, which are only indirectly related to the state of the atmosphere, are assimilated as optimally as possible. In this study, a detailed characterization of both retrievals and observed radiances for assimilation is provided, along with an error analysis. A method for assimilating remote sounding data while preserving its information content is presented. The main features of the technique are as follows: (i) the retrieval–forecast error cross covariance is removed even when the retrieval is severely constrained by a priori information, (ii) the radiative transfer calculations for radiance assimilation are done offline, and (iii) the number of assimilated quantities per observation is reduced to the ...

Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study shortrange forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed.

Evaluation of ozone total column measurements by the Ozone Monitoring Instrument using a data assimilation system

[1] On 15 July 2004, the Ozone Monitoring Instrument (OMI) on board the EOS Aura mission was launched. One of OMI’s priorities is to continue the record of high spatial resolution ozone total column measurements provided by the various Total Ozone Mapping Spectrometer (TOMS) instruments since 1978. To this end, it is essential to estimate the errors affecting OMI ozone total column measurements and to see whether the actual accuracy is consistent with estimated values before launch. In this paper, data assimilation techniques are used to create a large comparison data set composed of ozone analyses resulting from assimilation of standard meteorological observations and ozone retrievals (independent of OMI measurements) into a numerical weather prediction model. This data setprovidesexcellentglobalcoverageandtemporalresolution,notlimitedbythespatialand temporal distribution of other satellite or ground based information. The accuracy of the analyses is evaluated against ozone total column retrievals from Brewer measurements, while the assimilated ozone data set is compared to ozone predictions made using the ECMWF model, to check for the presence of bias. The OMI ozone column measurements considered here are obtained with the TOMS-V8 total ozone algorithm and denoted as OMTO3 columns. They are compared with simulated OMI ozone columns, i.e., the quantities that the TOMS-V8 algorithm would retrieve in the case when the atmospheric ozone profile at a specific location and time is equal to the one prescribed by the analysis. In this way, the comparison is statistically robust even when data acquired during a relatively short temporal interval or over a relatively small geographical area only is considered. A discussion of relevant error sources (including systematic components), vertical resolution, and contributions from prior information is provided. Special attention is given to determining the importance of representativeness errors. Our results show a solar zenith angle (SZA) dependence of the bias between measured and simulated OMI columns. This is believed to be due to moderate nonlinearity of the observation forward model and its effects on our definition of simulated OMI columns at high SZA. In view of these findings the final results of the intercomparison methodology used in this paper are obtained from OMI ozone columns retrieved using the basic implementation of the TOMS-V8 algorithm applied to measurements taken at SZA not exceeding 70. Intercomparison results between measured and simulated OMI ozone columns at SZA less than 70 show a relative bias of � 3.2 ± 3.1% and a root-mean-square error of 4.5 ± 1.5%. The resulting bias is consistent with available estimates of the bias ofOMTO3columnswithrespecttoSBUV/2between60Sand60N,aswellaswithrespect to global Dobson data and Brewer measurements between 30N and 60N.

Optimal Ensemble-Based Selection of Channels from Advanced Sounders in the Presence of Cloud

AbstractThis study aims to illustrate a general procedure based on well-known information theory concepts to select the channels from advanced satellite sounders that are most advantageous to assimilate both in clear-sky and overcast conditions using an ensemble-based estimate of forecast uncertainty. To this end, the standard iterative channel selection method, which is used to select the most informative channels from advanced infrared sounders for operational assimilation, was revisited so as to allow its use with measurements that have correlated errors. The method was here applied to determine a 24-humidity-sensitive-channel set that is small in size relative to a total of 8461 channels that are available on the Infrared Atmospheric Sounding Interferometer (IASI) on board the EUMETSAT Polar System MetOp satellites. The selected channels can be used to perform all-sky data assimilation experiments, in addition to those currently used for operational data assimilation of IASI data at ECMWF. Care was ta...

The GlobMODEL Demonstrator: Assimilation of New Satellite Products in an Operational Meteorological Center

As part of its Data User Element programme, the European Space Agency funded the GlobMODEL project which aimed at investigating the scientific, technical, and organizational issues associated with the use and exploitation of remotely-sensed observations, particularly from new sounders. A pilot study was performed as a ldquodemonstratorrdquo of the GlobMODEL idea, based on the use of new data, with a strong European heritage, not yet assimilated operationally. Two parallel assimilation experiments were performed, using either total column ozone or ozone profiles retrieved at the Royal Netherlands Meteorological Institute (KNMI) from the Ozone Monitoring Instrument (OMI). In both cases, the impact of assimilating OMI data in addition to the total ozone columns from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) on the European Centre for Medium Range Weather Forecasts (ECMWF) ozone analyses was assessed by means of independent measurements. We found that the impact of OMI total columns is mainly limited to the region between 20 and 80 hPa, and is particularly important at high latitudes in the Southern hemisphere where the stratospheric ozone transport and chemical depletion are generally difficult to model with accuracy. Furthermore, the assimilation experiments carried out in this work suggest that OMI DOAS (Differential Optical Absorption Spectroscopy) total ozone columns are on average larger than SCIAMACHY total columns by up to 3 DU, while OMI total columns derived from OMI ozone profiles are on average about 8 DU larger than SCIAMACHY total columns. At the same time, the demonstrator brought to light a number of issues related to the assimilation of atmospheric composition profiles, such as the shortcomings arising when the vertical resolution of the instrument is not properly accounted for in the assimilation. The GlobMODEL demonstrator accelerated scientific and operational utilization of new observations and its results prompted ECMWF to start the operational assimilation of OMI total column ozone data.