Jean-François Mahfouf

Analysis of Soil Moisture from Near-Surface Parameters: A Feasibility Study

Abstract The main purpose of this paper is to demonstrate that it is possible to estimate soil moisture from the evolution of atmospheric parameters near the surface (temperature and relative humidity) if a realistic surface transfer model is available. Two methods to initialize soil moisture in meteorological models are then proposed: a variational method where the optimal soil moisture minimizes a penality function and a sequential method consisting of a set of predictions and static corrections of soil moisture. The algorithms are examined with a one-dimensional model including a detailed land-surface parameterization. A feasibility study is undertaken using the HAPEX-MOBILHY dataset where soil moisture has been measured together with atmospheric parameters. It is demonstrated that for three 48-h clear-sky periods the two methods are able to converge rapidly toward a realistic soil moisture content starting from arbitrary values.

Evaluation of the Optimum Interpolation and Nudging Techniques for Soil Moisture Analysis Using FIFE Data

Abstract Initialization of land surface prognostic variables is a crucial issue for short- and medium-range forecasting as well as at seasonal timescales. In this study, two sequential soil moisture analysis schemes are tested, both based on the comparison between observed and predicted 2-m parameters: the nudging technique used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the optimum interpolation technique proposed by J. F. Mahfouf and used operationally at Meteo-France. Both techniques compute the soil moisture increments as a linear function of analysis increments of 2-m parameters (specific humidity at ECMWF, temperature and relative humidity at Meteo-France). Following the preliminary study by Y. Hu et al., the optimum interpolation technique has been adapted to the four soil-level ECMWF land surface scheme. Both methods are tested in the ECMWF single column model, which has been run for 4 months in 1987 at a grid point close to the location of the First Intern...

Issues Regarding the Assimilation of Cloud and Precipitation Data

Abstract The assimilation of observations indicative of quantitative cloud and precipitation characteristics is desirable for improving weather forecasts. For many fundamental reasons, it is a more difficult problem than the assimilation of conventional or clear-sky satellite radiance data. These reasons include concerns regarding nonlinearity of the required observation operators (forward models), nonnormality and large variances of representativeness, retrieval, or observation–operator errors, validation using new measures, dynamic and thermodynamic balances, and possibly limited predictability. Some operational weather prediction systems already assimilate precipitation observations, but much more research and development remains. The apparently critical, fundamental, and peculiar nature of many issues regarding cloud and precipitation assimilation implies that their more careful examination will be required for accelerating progress.

Variational Retrieval of Temperature and Humidity Profiles from TRMM Precipitation Data

Abstract This paper examines the performance of a one-dimensional variational (1DVAR) assimilation of Tropical Rainfall Measuring Mission satellite-derived surface rainfall rates from the Microwave Imager TMI. Temperature and specific humidity profiles are retrieved that are consistent with both observed and model short-range forecast rain rates. Two atmospheric situations are examined from ECMWF short-range forecasts at TL319L31 resolution. They encompass tropical cyclones, frontal bands, and mesoscale convective systems. Results show that 1DVAR is generally able to find modified profiles within the range of forecast errors (specified from the operational ECMWF statistics) that provide a precipitation field close to observations. Increments of temperature with respect to the background state are small indicating that 1DVAR essentially adjusts specific humidity to modify precipitation amounts. Consistency checks have been defined in order to discard profiles producing too large departures from the observe...

Four-Dimensional Variational Assimilation of Total Column Water Vapor in Rainy Areas

Abstract This paper studies the impact of assimilating rain-derived information in the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational (4DVAR) system. The approach is based on a one-dimensional variational (1DVAR) method. First, model temperature and humidity profiles are adjusted by assimilating observed surface rain rates in 1DVAR. Second, 1DVAR total column water vapor (TCWV) estimates are assimilated in 4DVAR. Observations used are Tropical Rainfall Measuring Mission (TRMM) surface rain-rate estimates from the TRMM Microwave Imager. Two assimilation experiments making use of 1DVAR TCWV were run for a 15-day period. The “Rain-1” experiment only assimilates 1DVAR retrievals where the observed rain rate is nonzero while the “Rain-2” experiment assimilates all 1DVAR TCWV estimates. The period selected includes Hurricane Bonnie, which was well sampled by TRMM (late August 1998). Results show a positive impact on the humidity analysis of assimilating 1DVAR TCWV in 4DV...

Sensitivity of latent heat flux from PILPS land-surface schemes to perturbations of surface air temperature

In the PILPS Phase 2a experiment, 23 land-surface schemes were compared in an off-line control experiment using observed meteorological data from Cabauw, the Netherlands. Two simple sensitivity experiments were also undertaken in which the observed surface air temperature was artificially increased or decreased by 2 K while all other factors remained as observed. On the annual timescale, all schemes show similar responses to these perturbations in latent, sensible heat flux, and other key variables. For the 2-K increase in temperature, surface temperatures and latent heat fluxes all increase while net radiation, sensible heat fluxes, and soil moistures all decrease. The results are reversed for a 2-K temperature decrease. The changes in sensible heat fluxes and, especially, the changes in the latent heat fluxes are not linearly related to the change of temperature. Theoretically, the nonlinear relationship between air temperature and the latent heat flux is evident and due to the convex relationship between air temperature and saturation vapor pressure. A simple test shows that, the effect of the change of air temperature on the atmospheric stratification aside, this nonlinear relationship is shown in the form that the increase of the latent heat flux for a 2-K temperature increase is larger than its decrease for a 2K temperature decrease. However, the results from the Cabauw sensitivity experiments show that the increase of the latent heat flux in the 12-K experiment is smaller than the decrease of the latent heat flux in the 22-K experiment (we refer to this as the asymmetry). The analysis in this paper shows that this inconsistency between the theoretical relationship and the Cabauw sensitivity experiments results (or the asymmetry) is due to (i) the involvement of the bg formulation, which is a function of a series stress factors that limited the evaporation and whose values change in the 62-K experiments, leading to strong modifications of the latent heat flux; (ii) the change of the drag coefficient induced by the changes in stratification due to the imposed air temperature changes (62 K) in parameterizations of latent heat flux common in current land-surface schemes. Among all stress factors involved in the bg formulation, the soil moisture stress in the 12-K experiment induced by the increased evaporation is the main factor that contributes to the asymmetry.

Error analysis of TMI rainfall estimates over ocean for variational data assimilation

An intercomparison of retrieval errors from different Tropical Rainfall Measuring Mission (TRMM) passive microwave rainfall products was carried out to assess the definition of observation error for experiments of rainfall assimilation in a variational framework. Depending on algorithms and their spatial resolution and sampling, a large variety of error estimates occurred. The error propagation to the European Centre for Medium-Range Weather Forecasts (ECMWF) model grid (here 45 and 60 km) was investigated from error simulations and observed data with and without accounting for spatial error correlation. n n n nAll algorithms used in this study (TRMM standard product 2A12 V.5 and two alternative algorithms, namely PATER and BAMPR) employ a Bayesian retrieval framework. The Bayesian errors obtained from each algorithm from different case-studies showed values well above 100% at low rain rates (0.1 mm h−1) and around 50% at high rain rates (20–50 mm h−1) at the original product resolution and sampling. These Bayesian errors corresponded very well with those from an independent evaluation which was carried out by comparing TRMM microwave radiometer (TMI) estimates to precipitation radar retrievals at the same (here ≈27×40 km2) resolution. n n n nThe impact of spatial averaging on retrieval errors was simulated using fits to the Bayesian errors and realistic log-normal rainfall probability distributions. By neglecting spatial correlation, the range of errors is reduced from 70–200% to 20–50% at low rain rates and from 25–70% to 5–20% at high rain rates. To account for spatial data correlation, TMI observations were first averaged to the ECMWF model grid. Then the decorrelation of rain rates as a function of separation distance from all products was calculated. The introduction of spatial error correlation affected both error reduction and dispersion of errors per rain-rate interval. The final error estimates ranged from 50–150% at low rain rates to 20–50% at high rain rates. The analysis suggests that once the spatial correlation pattern of a product is known, the probability density distribution of real observations inside the model grid does not produce larger scatter and therefore a simple scaling may suffice to calculate rainfall retrieval errors at the model resolution. Copyright

Coupling of moist-convective and stratiform precipitation processes for variational data assimilation

Abstract Some problems posed by the coupling of moist-convective and stratiform precipitation processes for variational assimilation of precipitation-rate data are examined in a 1D-Var framework. Background-error statistics and vertical resolution are chosen to be representative of current operational practice. Three advanced parameterization schemes for moist-convection are studied: the relaxed Arakawa–Schubert (RAS) scheme, Tiedtke’s mass-flux scheme (operational at the European Centre for Medium-Range Weather Forecasts), and the Betts–Miller scheme. Both fractional-stepping and process-splitting approaches for combining physical processes are examined. The behavior of the variational adjustment for background profiles of temperature and specific humidity in the neighborhood of saturation is of particular interest. In the 1D-Var context examined here, it is demonstrated that the introduction of the stratiform precipitation process can have a negative impact on the minimization in the sense that, even wh...

Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with oth...

A Study of Rainfall Interception Using a 1And Surface Parameterization for Mesoscale Meteorological Models

Abstract Rainfall interception by vegetation canopies is studied using a parameterization of land surface Processes for mesoscale meteorological models. The interception scheme allows for a single vegetation canopy, and manages interception through a prognostic variable representing the amount of liquid water retained by the foliage. A set of 24 h simulation fully interactive with the boundary layer, is carried out with a one-dimensional model in order to examine the sensitivity of the interception scheme to vegetation properties. The evaporation from the interception reservoir is strongly enhanced by high values of the roughness length. The leaf area index, acting on the maximum storm capacity, modifies the drying time of the foliage. As a first stage of validation, the interception scheme is compared with other models developed for hydrological purposes. It appears that the scheme is not very different from the single-layer Rutter model, which has been well tested and validated. Only minor differences a...