A midlatitude precipitating cloud database validated
with satellite observations

Jean-Pierre Chaboureau, Nathalie Söhne, Jean-Pierre Pinty, Ingo
Meirol-Mautner, Eric Defer, Catherine Prigent, Juan Pardo, Mario Mech,
Susanne Crewell

► To cite this version:

Jean-Pierre Chaboureau, Nathalie Söhne, Jean-Pierre Pinty, Ingo Meirol-Mautner, Eric Defer, et al. A midlatitude precipitating cloud database validated with satellite observations. Journal of Applied Meteorology and Climatology, 2008, 47, pp.1337-1353. 10.1175/2007JAMC1731.1.1. hal-00326170

HAL Id: hal-00326170

https://hal.science/hal-00326170v1

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# A Midlatitude Precipitating Cloud Database Validated with Satellite Observations

JEAN-PIERRE CHABOUREAU, NATHALIE SÖHNE, AND JEAN-PIERRE PINTY

*Laboratoire d'Aérologie, University of Toulouse, and CNRS, Toulouse, France*

INGO MEIOLD-MAUTNER, ERIC DEFER, AND CATHERINE PRIGENT

LERMA, Observatoire de Paris, Paris, France

JUAN R. PARDO

*Instituto de Estructura de la Materia, CSIC, Madrid, Spain*

MARIO MECH AND SUSANNE CREWELL

*Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany*

*(Manuscript received 6 March 2007, in final form 20 August 2007)*

## ABSTRACT

The simulations of five midlatitude precipitating events by the nonhydrostatic mesoscale model Méso-NH are analyzed. These cases cover contrasted precipitation situations from 30° to 60°N, which are typical of midlatitudes. They include a frontal case with light precipitation over the Rhine River area (10 February 2000), a long-lasting precipitation event at Hoek van Holland, Netherlands (19 September 2001), a moderate rain case over the Elbe (12 August 2002), an intense rain case over Algiers (10 November 2001), and the “millennium storm” in the United Kingdom (30 October 2000). The physically consistent hydrometeor and thermodynamic outputs are used to generate a database for cloud and precipitation retrievals. The hydrometeor vertical profiles that were generated vary mostly with the 0°C isotherm, located between 1 and 3 km in height depending on the case. The characteristics of this midlatitude database are complementary to the GPROF database, which mostly concentrates on tropical situations. The realism of the simulations is evaluated against satellite observations by comparing synthetic brightness temperatures (BTs) with Advanced Microwave Sounding Unit (AMSU), Special Sensor Microwave Imager (SSM/I), and Meteosat observations. The good reproduction of the BT distributions by the model is exploited by calculating categorical scores for verification purposes. The comparison with 3-hourly Meteosat observations demonstrates the ability of the model to forecast the time evolution of the cloud cover, the latter being better predicted for the stratiform cases than for others. The comparison with AMSU-B measurements shows the skill of the model to predict rainfall at the correct location.

## 1. Introduction

Research efforts are continuing with the aim of improving the modeling of cloud and precipitation processes, for both climate monitoring and weather forecasting. As for many geophysical variables, the observation of clouds and precipitation is possible on a global scale by remote sensing only from space. In particular,

retrieving rain rates is a motivation of passive microwave measurements from satellites in low earth orbit such as the Special Sensor Microwave Imager (SSM/I) operational series and the Tropical Rainfall Measuring Mission (TRMM). Future programs are envisioned to observe global precipitation more frequently and more accurately by using a constellation of passive microwave radiometers as in the Global Precipitation Measurement (GPM) or by developing systems capable of observing in the submillimeter spectral range from geostationary platforms.

Microwave measurements do not directly sense surface rain rates but are often sensitive to the full atmo-

<footnote>*Corresponding author address:* Dr. Jean-Pierre Chaboureau, Laboratoire d'Aérologie, University of Toulouse/CNRS, Observatoire Midi-Pyrénées, 14 av. Belin, F-31400 Toulouse, France. E-mail: jean-pierre.chaboureau@aero.obs-mip.fr</footnote>
spheric column, including the various cloud layers. Precipitating cloud databases have been built to investigate the relationship between space-borne measurements and rainfall (e.g., Panegrossi et al. 1998; Kummerow et al. 2001; Bauer 2001). These precipitating cloud databases are comprised of thousands of physically consistent hydrometeor and thermodynamic profiles obtained from cloud-resolving model simulations. Brightness temperatures (BTs) are computed from these simulated cloud profiles, using a radiative transfer model (RTM). The relationships between the atmospheric variables in the model and the simulated BTs are then used to develop inversion procedures to retrieve cloud and precipitation fields from a set of satellite observations. An advantage of these mesoscale databases is that they provide profiles that have a more detailed description of the microphysics than the low-resolution numerical weather prediction (NWP) model can give, and that are associated with realistic synthetic BTs obtained from state-of-the art RTMs.

The existing databases mainly sample tropical situations under convective conditions. For example, the Goddard Profiling (GPROF) database was built to retrieve rain from both SSM/I and TRMM observations. As noted by Kummerow et al. (2001), all the model simulations in the GPROF database (its first version) are tropical in nature and, in most, stratiform rain events are represented in close proximity to convection. As a consequence, such databases cannot directly be used to develop algorithms for rainfall estimates outside the tropics. [Note, however, that the latest version of the GPROF database also contains two midlatitude simulations (Olson et al. 2006).] This motivated us to perform realistic simulations for a variety of extratropical environments. Furthermore, surface rain retrieval methods are very sensitive to the database from which the inversion algorithm is generated. For example, Medaglia et al. (2005) investigate this issue for two models having different bulk microphysical schemes showing significant differences in the retrieved rain rates. This underlines the need to evaluate the simulated database, in particular with the existing satellite observations.

In this study, we propose a database of midlatitude profiles obtained from situations over Europe and the Mediterranean Sea simulated by the nonhydrostatic mesoscale model Méso-NH (Lafore et al. 1998). This database can be used for many purposes, including to test the ability of the Méso-NH model coupled with radiative transfer codes to simulate realistic BTs (Meirold-Mautner et al. 2007), to quantify the skill of the model to forecast midlatitude rain events (this study), to retrieve hydrometeor contents from existing

satellite observations, and to investigate the capabilities of future sensors in the submillimeter range (Mech et al. 2007; Defer et al. 2008).

Five typical midlatitude cases have been identified. They cover large domains in the latitudes of 30°–60°N and provide a large number of heterogeneous profiles with various microphysical compositions. The cases correspond to real meteorological conditions, allowing an evaluation of the quality of the simulated hydrometeor fields by comparison with coincident satellite observations. This is the model-to-satellite approach (Morcrette 1991) in which the satellite BTs are directly compared to the BTs that were computed from the predicted model fields. Using this method, the meteorological model coupled with the radiative transfer code can be evaluated before developing any rainfall retrieval from the simulated database. Previous studies have assessed the Méso-NH model cloud scheme in terms of cloud cover and hydrometeor contents by comparison with Meteosat (Chaboureau et al. 2000, 2002; Meirold-Mautner et al. 2007), Geostationary Operational Environmental Satellite (GOES; Chaboureau and Bechtold 2005), TRMM Microwave Imager (TMI; Wiedner et al. 2004), SSM/I, and Advanced Microwave Sounding Unit (AMSU; Meirold-Mautner et al. 2007) observations. The model-to-satellite approach associated with the BT difference technique applied to Meteosat Second Generation observations can also verify specific forecasts such as cirrus cover (Chaboureau and Pinty 2006), dust occurrence (Chaboureau et al. 2007b), and convective overshoots (Chaboureau et al. 2007a). Here, the evaluation is performed by comparison with observations from the Meteosat Visible and InfraRed Imager (MVIRI), the SSM/I hosted by the Defense Meteorological Satellite Program's satellites, and the AMSU on board National Oceanic and Atmospheric Administration (NOAA) satellites. The channels most sensitive to cloud and precipitation fields were selected: 11 µm in the infrared region and 37, 85, 89, and 150 GHz in the microwave region.

The model was initialized with standard analyses on a 40-km grid mesh. Two-way interactive grid nesting was used for downscaling from the synoptic scale to the convective scale to be resolved. Typical tropical precipitating cases require a one kilometer mesh to represent the convective updrafts and the associated microphysical fields explicitly. In contrast, midlatitude rain events are often more stratiform and their vertical circulation can easily be captured on a mesh with a ≥10-km spacing (but embedded convection in frontal rainbands needs a finer mesh to be represented realistically). Here, the setup of the simulations depended on
<table><caption>TABLE 1. General characteristics for the simulations.</caption><tr><td>Nesting geometry</td><td>Three models<sup>a</sup></td></tr><tr><td>Nested grid spacing</td><td>40, 10, 2.5 km<sup>b</sup></td></tr><tr><td>Vertical grid</td><td>50 stretched levels with Δz from 60 to 600 m</td></tr><tr><td>Model top</td><td>20 km</td></tr><tr><td>Physical parameterizations<sup>c</sup></td><td></td></tr><tr><td>Microphysics</td><td>Bulk scheme, five hydrometeor species: cloud water, rainwater, pristine ice, snow, graupel (Pinty and Jabouille 1998)</td></tr><tr><td>Radiation</td><td>ECMWF package (Gregory et al. 2000)</td></tr><tr><td>Turbulence</td><td>1.5-order scheme (Cuxart et al. 2000)</td></tr><tr><td>Surface</td><td>Interaction between soil, biosphere, and atmosphere scheme (Noilhan and Planton 1989)</td></tr></table>

<sup>a</sup> Two models only for the UKMIL case.

<sup>b</sup> Here, 40 and 13 km are for the UKMIL case.

<sup>c</sup> These physical parameterizations correspond to the high-resolution model. Physical parameterizations for the coarse-resolution models are the same but with the addition of a convective scheme (Bechtold et al. 2001).

the meteorological case. However, all the model outputs were analyzed on the 10-km grid mesh, which was comparable with the spatial resolution of the satellite microwave observations used in this study. This setup allows us to present an original application of the model-to-satellite approach by calculating categorical scores from observed and simulated BTs.

The paper is organized as follows. Section 2 presents the Méso-NH model and its mixed-phase microphysical scheme, together with the radiative codes used to calculate the BTs. Section 3 contains an overview of the cases that compose the database. Section 4 describes the variability of the database in terms of cloud and precipitation fields. The database is also contrasted with the GPROF tropical database. Section 5 evaluates the simulations by comparing the simulated BTs from Méso-NH outputs with the observed BTs from Meteosat, SSM/I, and AMSU-B. Section 6 concludes the paper.

## 2. Meteorological and radiative transfer models

### a. Méso-NH model and setup

Méso-NH is a nonhydrostatic mesoscale model, jointly developed by Météo-France and the Centre National de la Recherche Scientifique (CNRS). Its general characteristics and the specific parameters for this study are summarized in Table 1. A detailed description of Méso-NH is given in Lafore et al. (1998) and the mixed-phase microphysical scheme developed by Pinty and Jabouille (1998) is described in detail in the next subsection.

Five numerical experiments are discussed in this study (Table 2). For all of them, temperature, winds, surface pressure, water vapor, and sea surface temperature taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis at syn-

optic times (0000, 0600, 1200, and 1800 UTC) are used as initial and boundary conditions. All the simulations start at 0000 UTC and use the two-way grid-nesting technique (Stein et al. 2000). The same parameterized physics are used for all the nested grids, except for convection parameterization, which is not activated in the innermost grid (explicit cloud only). Results presented here are from the second grid only at a 10-km resolution. The second grid covers 1600 km × 1600 km for the Rhine area (RHINE), Hoek van Holland, the Netherlands (HOEK), and the Elbe River area (ELBE) cases, 2000 km × 1500 km for the Algiers (ALGER) case, and 2340 km × 2106 km for the so-called United Kingdom millennium storm (UKMIL) case. Two output times are selected for each case, corresponding to the AMSU and SSM/I pass times (Table 2).

### b. Summary of the mixed-phase microphysical scheme

The calculations essentially follow the approach of Lin et al. (1983): a three-class ice parameterization is used with Kessler's scheme for the warm processes. As illustrated in Fig. 1, the scheme predicts the evolution of the mixing ratios of six water species: $r_v$ (vapor), $r_c$, and $r_r$ (cloud droplets and raindrops), and $r_i$, $r_s$, and $r_g$ (pristine ice, snow/aggregates, and frozen drops/graupel defined by an increasing degree of riming, respectively). The concentration of the pristine ice crystals, here assumed to be plates, is diagnosed. The concentration of the precipitating water drops and ice crystals (snow and graupel) is parameterized according to Caciaux et al. (1994), with the total number concentration $N$ given by

$$N = C\lambda^x, \quad (1)$$

where $\lambda$ is the slope parameter of the size distribution and C and x are empirical constants derived from radar
<table><caption>TABLE 2. Overview of the simulation cases (ID is identifier).</caption><thead><tr><th>Name</th><th>Event</th><th>Date</th><th>AMSU ID (time)</th><th>SSM/I ID (time)</th></tr></thead><tbody><tr><td>RHINE</td><td>Light precipitation over the Rhine</td><td>10 Feb 2000</td><td>N15 (1800 UTC)</td><td>F14 (0900 UTC)</td></tr><tr><td>HOEK</td><td>Light rain at Hoek van Holland</td><td>19 Sep 2001</td><td>N15 (1800 UTC)</td><td>F14 (0900 UTC)</td></tr><tr><td>ELBE</td><td>Elbe flood</td><td>12 Aug 2002</td><td>N15 (0600 UTC)</td><td>F14 (1800 UTC)</td></tr><tr><td>ALGER</td><td>Algiers flood</td><td>10 Nov 2001</td><td>N15 (0200 UTC)</td><td>F14 (0700 UTC)</td></tr><tr><td>UKMIL</td><td>Millennium storm</td><td>30 Oct 2000</td><td>N15 (0900 UTC)</td><td>F13 (0600 UTC)</td></tr></tbody></table>

observations. The size distribution of the hydrometeors is assumed to follow a generalized $\gamma$ law:

$$ 
\begin{aligned}
n(D)dD &= Ng(D)dD \\
&= N \frac{\alpha}{\Gamma(\nu)} \lambda^{\alpha\nu} D^{\alpha\nu-1} \exp[-(\lambda D)^{\alpha}] dD,
\end{aligned} 
\quad (2) 
$$

where $g(D)$ is the normalized form that reduces to the Marshall-Palmer law when $\alpha = \nu = 1$ ($D$ is the diameter of the drops or the maximal dimension of the particles). Finally, simple power laws are taken for the mass-size relationship ($m = aD^b$) and the velocity-size relationship ($\nu = cD^d$) to perform useful analytical integrations using the moment formula:

$$ M(p) = \int_{0}^{\infty} D^{p} g(D) dD = \frac{\Gamma(\nu + p/\alpha)1}{\Gamma(\nu)} \frac{1}{\lambda^{p}}, \quad (3) $$

where $M(p)$ is the $p$th moment of $g(D)$. A first application of Eq. (3) is to compute the mixing ratio $r_x$ as

$$ \rho r_x = a N M_x(b). \quad (4) $$

Table 3 provides the complete characterization of each ice category and the cloud droplets/raindrops.

Hydrometeors are formed and destroyed according to the processes depicted in Fig. 1. The warm part of the scheme (Kessler scheme) includes the growth of cloud droplets by condensation and the formation of rain by autoconversion. Raindrops grow by accretion (ACC) or evaporate in subsaturated areas.

In the cold part of the scheme, the pristine ice category is initiated by homogeneous nucleation (HON), when $T \le -35^\circ\text{C}$, or more frequently by heterogeneous nucleation (HEN), so the small ice crystal concentration is a simple function of the local supersaturation over ice. These crystals grow by water vapor deposition (DEP) and by the Bergeron-Findeisen effect (BER). The snow phase is formed by AUT of the primary ice crystals; it grows by DEP of water vapor, by aggregation (AGG) through small crystal collection, and by the

<figure><img src="image_2.png" /><figcaption>FIG. 1. Microphysical processes included in the mixed-phase scheme (see text for the acronyms and explanations).</figcaption></figure>
<table><caption>TABLE 3. Characteristics of each hydrometeor category.*</caption><thead><tr><th>Parameters</th><th>r<sub>i</sub></th><th>r<sub>s</sub></th><th>r<sub>g</sub></th><th>r<sub>c</sub></th><th>r<sub>r</sub></th></tr></thead><tbody><tr><td>α</td><td>3</td><td>1</td><td>1</td><td>3</td><td>1</td></tr><tr><td>ν</td><td>3</td><td>1</td><td>1</td><td>3</td><td>1</td></tr><tr><td>a</td><td>0.82</td><td>0.02</td><td>19.6</td><td>524</td><td>524</td></tr><tr><td>b</td><td>2.5</td><td>1.9</td><td>2.8</td><td>3</td><td>3</td></tr><tr><td>c</td><td>800</td><td>5.1</td><td>124</td><td>3.210<sup>7</sup></td><td>842</td></tr><tr><td>d</td><td>1.00</td><td>0.27</td><td>0.66</td><td>2</td><td>0.8</td></tr><tr><td>C</td><td></td><td>5</td><td>5 10<sup>5</sup></td><td></td><td>10<sup>7</sup></td></tr><tr><td>x</td><td></td><td>1</td><td>-0.5</td><td></td><td>-1</td></tr></tbody></table>

light riming produced by the impaction of cloud droplets (RIM) and raindrops (ACC). Graupel are formed as a consequence of the heavy riming of snow (RIM and ACC) or by rain freezing (CFR) when supercooled raindrops come into contact with pristine ice crystals. The distinction between light and heavy riming is made on the basis of a critical size of the snowflakes (droplets) or by estimation of the mean density of the resulting particles (raindrops). According to the heat balance equation, graupel can grow more efficiently in the wet (WET) mode than in the dry (DRY) mode when riming is very intense (as for hailstone embryos). In the latter case, the excess of nonfreezable liquid water at the surface of the graupel is shed to form raindrops. When T ≥ 0°C, pristine crystals immediately melt into cloud droplets (MLT), while snowflakes are progressively converted into graupel that melt as they fall. Each condensed water species has a nonzero fall speed except for cloud droplets.

### c. Radiative transfer models

Synthetic BTs corresponding to the Meteosat-7 infrared channel in the thermal infrared window (10.5–12.5 µm, referred to as 11 µm) were computed using the Radiative Transfer for the Tiros Operational Vertical Sounder (RTTOV) code, version 8.7 (Saunders et al. 2005). In the infrared, the RTTOV code takes clouds into account as gray bodies (Chevallier et al. 2001). Hexagonal columns are assumed with radiative properties taken from Baran and Francis (2004) and with an effective dimension diagnosed from the ice water content (McFarquhar et al. 2003). The surface emissivity over land is given by the Ecoclimap database (Masson et al. 2003).

In the microwave region, BTs were simulated using the Atmospheric Transmission at Microwaves (ATM) model (Pardo et al. 2001; Prigent et al. 2001). Absorp-

tion by atmospheric gases was introduced according to Pardo et al. (2001) and scattering by hydrometeors was computed following the T-matrix approach of Mishchenko (1991). The sensitivity of the radiative transfer model to the characteristics of the frozen particles (size, density, dielectric properties) for the microphysics database presented here has been carefully analyzed in Meirold-Mautner et al. (2007). Spherical shapes are assumed for all the particles as the BT sensitivity to the shape of the frozen particles is weak in contrast with their other characteristics. The snow scattering properties derived from Liu (2004) were adopted for a good agreement between the simulations and the satellite observations. The model includes a full treatment of the effect of the surface. An emissivity model was implemented for the wind-roughened ocean surface (Guillou et al. 1996). A land surface emissivity atlas derived from SSM/I and AMSU observations was attached to the radiative transfer code (Prigent et al. 1997, 2005, 2006), along with angular and frequency parameterizations.

Note that the BTs can be simulated in the microwave region using RTTOV, version 8.7. However, at these frequencies, RTTOV takes only two species of precipitating hydrometeors into account (viz., rain and graupel), whereas the BT sensitivity to scattering by snow is dramatic at 89 and 150 GHz. So the snow effects on BTs need to be analyzed and simulated correctly, as in Meirold-Mautner et al. (2007).

### 3. Case studies

The five cases were typical of midlatitude events. They occurred in autumn, summer, and winter in southern and northern parts of Europe and covered both land and sea. Their associated surface rain rates and pressure at mean sea level are displayed for the AMSU output times in Fig. 2. The instances included a frontal case with light precipitation over the Rhine area (10 February 2000; Fig. 2a), a long-lasting precipitation event at Hoek van Holland (19 September 2001; Fig. 2b), a moderate rain case over the Elbe (12 August 2002; Fig. 2c), an intense rain case over Algiers (10 November 2001; Fig. 2d), and the millennium storm in the United Kingdom (30 October 2000, Fig. 2e). All these cases concerned cloud systems organized at the mesoscale.

For the RHINE case, light precipitation was related to a cold front passing West Germany on 10 February 2000. At 1800 UTC, the cold front was associated with a broad pattern of light surface rainfall of a few millimeters h<sup>-1</sup> (Fig. 2a). The 0°C height dropped from 2 to 0.5 km, which was of interest for the precipitation-phase retrieval.

Light precipitation occurred on 19 September 2001 in

<footnote>* Coefficients α and ν are used in Eq. (2). The other coefficients are related to power-law relationships for the mass (m = aD<sup>b</sup>) and the fall speed (v = cD<sup>d</sup>), where D is the particle size, and for the concentration in Eq. (1). All variables are in MKS units.</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 2. Surface rainfall (shading; mm h⁻¹) and pressure at mean sea level (contour every 4 hPa) simulated by Méso-NH over the second grid for the various cases and the times corresponding to the nearest hour when an AMSU pass occurred (see Table 2).</figcaption></figure>
the HOEK case. This was a long-lasting precipitation event produced by a quasi-stationary low pressure system over the Netherlands (Fig. 2b). A maximum of 100 mm of accumulated rainfall was recorded over the whole event at Hoek van Holland, with relatively small rain rates of a few millimeters $h^{-1}$.

The Elbe River flood case (ELBE) involved convection embedded within synoptic-scale frontal precipitation that resulted in the Elbe flood in August 2002. The synoptic situation was characterized by a deep cyclone moving from the Mediterranean Sea toward Poland (e.g., Zängl 2004). On 12 August 2002, the cyclone was quasi stationary over eastern Germany and the Czech Republic. On the western side of the low, the partly occluded warm front coincided with the steepest pressure gradient area at 0600 UTC (Fig. 2c). It brought large amounts of rainfall: more than 300 mm fell in one day in parts of Erzgebirge, the mountain range at the German-Czech frontier. The extreme precipitation was followed by a very quick rise of the levels of the Elbe River tributaries, leading to a centennial Elbe flood with the largest-recorded flood-related damage in Europe.

The ALGER flood case occurred on 10 November 2001 leading to the most devastating flood in this area with more than 700 casualties and catastrophic damage (e.g., Tripoli et al. 2005; Argence et al. 2006). The rainfall was caused by an intense mesoscale cyclone resulting from the interaction between an upper-level trough over Spain and lower-level warm air moving north off the Sahara. At 0200 UTC the heaviest rainfalls were located in several cells organized in a line along the North African coast (Fig. 2d). Over Algiers, 262 mm of rainfall was measured during the entire storm episode with more than 130 mm in only 3 h, between 0600 and 0900 UTC 10 November 2001, whereas only 41 mm was recorded at the Dar-el-Bedia station, situated inland only 15 km away from Algiers (Argence et al. 2006).

The UKMIL corresponded to an exceptionally intense low over the English Midlands and its associated fronts. On 30 October 2000 the low had deepened from 994 to 958 hPa in 12 h (Browning et al. 2001). The steep pressure gradient resulted in strong winds and widespread gusts between 30 and 40 m s$^{-1}$. Heavy rain fell all night, leading to 24-h totals between 25 and 50 mm, with ≥ 75 mm in some areas. Local floods occurred and caused major disruption of commuter traffic during the morning rush hour of 30 October 2000. The rainfall pattern was typical of an extratropical cyclone at 0900 UTC (Fig. 2e). The more intense areas were located in the occluded warm fronts and trailing cold fronts of the low over the North Sea, while weak showers were scattered in the cold sector over the Atlantic Ocean.

# 4. Cloud and precipitation variability

## a. Overview

The distributions of the vertically integrated hydrometeor contents and the surface precipitation rates are first examined (Fig. 3). For the sake of clarity, the outputs are shown at the AMSU times only. The distribution of the surface precipitation rate shows a large variability that includes light (RHINE and HOEK), moderate (ELBE and UKMIL), and strong (ALGER) precipitation cases, with maximum values of 8, 25, and 40 mm $h^{-1}$, respectively. The partitioning of the cases into the same three groups was also found for the integrated ice, snow, and graupel contents. In contrast, the distribution of the rain content fell into two groups only (RHINE and HOEK versus ELBE, UKMIL, and ALGER) and the distribution of the liquid water content was more homogeneous. This can be explained by the microphysics and the formation of the hydrometeors. An excess of ice cloud was converted into snow that grew by aggregation and riming and was then transformed into graupel particles. Finally, graupel particles and raindrops contributed the most to the surface precipitation rate.

The surface precipitation rate is the result of a number of complex processes including vertical velocity and humidity supply to the diverse microphysical processes. Therefore, the relation between the precipitation rate at the surface and the hydrometeor distribution aloft is not straightforward. An illustration is given in Fig. 4 in which the vertical hydrometeor profiles averaged over the simulation domain are drawn. Overall, the distribution of nonprecipitating hydrometeors strongly depends on the 0°C isotherm. As the simulation domains cover a few thousand kilometers, the altitude of the 0°C isotherm changes by a few hundred meters as indicated by the range drawn on each series of profiles (Fig. 4). Nonprecipitating ice content is found only above the 0°C isotherm maximum height as the primary ice crystals are immediately melted into cloud droplets at temperatures warmer than 0°C. In contrast, cloud water can exist well above the freezing level in the form of supercooled droplets, which are available for ice riming. Precipitating ice can also be found below the 0°C isotherm in warm layers in which the snowflakes are progressively converted into graupel particles that melt as they fall. Rain is formed by autoconversion of cloud droplets or results from the melting of graupel. As a consequence, the rain layer is always below the 0°C isotherm.

The averaged vertical profiles also varied from case to case (Fig. 4). This was mostly because of the seasonal
<figure><img src="image_4.png" /><figcaption>FIG. 3. Distributions of vertically integrated hydrometeor contents (kg m⁻²) and precipitation rates (mm h⁻¹) for the various cases at the AMSU output times. The bin widths of the ice, snow, graupel, cloud liquid water, and rainwater contents are 0.05, 0.15, 0.3, 0.1, and 0.2 kg m⁻², respectively, and the precipitation rate is 2 mm h⁻¹.</figcaption></figure>

variation of the air temperature. The RHINE case in February included grid points where graupel and snow particles could reach the ground. The two autumn cases (UKMIL and ALGER) presented similar shapes with snow and graupel layers above the ground. The HOEK case in September displayed higher precipitating frozen hydrometeors (above 1.5 km). Finally, the ELBE case in August was the warmest, with a deep cloud water

layer extending up to 4 km and frozen water content present above 2.5 km.

The series of vertical distributions of Fig. 4 clearly shows that the precipitation was produced by cold processes with the formation of intermediate snow and graupel particles that later melted into rain. A large number of methods to estimate surface precipitation from microwave observations, especially at high fre-

<figure><img src="image_5.png" /><figcaption>FIG. 4. Mean hydrometeor vertical profiles for the different cases at the AMSU output times. Averages are calculated only from hydrometeor contents that are not null. The horizontal thick (thin) line represents the mean (extreme) altitude(s) of the 0°C isotherm.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>FIG. 5. Correlation of surface precipitation rate with the different integrated hydrometeor contents for the various cases at the two output times.</figcaption></figure>

quotencies, is based on the statistical relationship between the upper-atmospheric ice particles and the surface precipitation rate (e.g., Spencer et al. 1989; Grody 1991; Ferraro and Marks 1995). Such a relationship was investigated by looking at the correlation between the surface precipitation rate and the different integrated hydrometeor contents at two output times (Fig. 5). As expected, the highest correlation existed with the vertically integrated rain (up to 0.9). Linear correlation coefficients above 0.7 were also found for the integrated graupel content, but for three cases only. Lower values were obtained for vertically integrated snow, which was more strongly case dependent. The correlation relative to the integrated nonprecipitating water (ice and cloud water) content was the lowest (around 0.5). It should also be pointed out that the correlation values for a particular case and a particular water content can vary considerably with time. For example, the correlation coefficients with the integrated graupel content for the ELBE case were 0.76 and 0.39 at 0600 and 1800 UTC, respectively. This makes rain retrieval from indirect measurements of cloud and precipitation contents, using regression-based methods, highly challenging.

## b. Midlatitude versus tropical databases

The mean vertical profiles of these midlatitude situations differ significantly from those found in tropical conditions. First, the freezing level is located between 1 and 3 km in altitude, while it is usually as high as 4.5 km in the tropics. This means that the frozen hydrometeors in this database are present at the first levels above the

surface. Then, these midlatitude cases are mostly stratiform in nature. Therefore, they sample meteorological conditions with a weak vertical velocity that favors small-size hydrometeors (snow and light rain), in contrast to tropical deep convective situations that are more favorable to the growth of large graupel particles and big raindrops.

An illustration of the difference in characteristics between midlatitude and tropical databases can be seen by comparing the distribution of the current database with that of the GPROF. The latter includes six cloud-resolving model simulations in its latest version [four tropical and two midlatitude simulations; Olson et al. (2006)]. Results are shown in a conserved-variable diagram with equivalent potential temperature ($\theta_e$) as abscissa and total water content ($r_T$) as ordinate obtained from all the tropospheric levels (Fig. 6). This diagram is commonly used for examining mixing processes within clouds. A typical individual sounding presents an $r_T$ that decreases with altitude and an $\theta_e$ minimum at midlevels. In the current database, most of the grid points display values in the top left-hand corner, with $\theta_e < 330$ K and $r_T < 14 \text{ g kg}^{-1}$ (Fig. 6a). In contrast, the distribution of the GPROF database is shifted toward the bottom right. In particular, at low altitudes, $\theta_e$ and $r_T$ present larger values than the current database, around 350 K and $18 \text{ g kg}^{-1}$, respectively (Fig. 6b).

## 5. Cloud database evaluation

The quality of the simulated cloud and precipitation fields will now be examined. This is done objectively by comparing simulations with satellite observations using the model-to-satellite approach. The frequency ranges considered here record different cloud properties. The $11$-µm channel is mainly sensitive to the cloud-top temperature. At 37 GHz, emission from cloud liquid water is significant compared to the cold oceanic background. In contrast, the BTs at 150, 89, and 85 GHz decrease with the hydrometeor columns because of scattering by large ice particles (snow and graupel). In the following, an example of observed and simulated BTs is first given. Then the BT distributions of all the cases are compared. Finally, two objective verifications of the cloud cover and rain forecasts are performed.

### a. Visual inspection of BT maps

As an example, the observed and simulated BT maps for the ELBE case are shown in Fig. 7. Observations from the $11$-µm Meteosat channel show the high- and midlevel cloud cover with BTs of less than 260 K, which rolls around the low centered over central Europe, from Slovakia to Croatia. Elsewhere, BTs greater than 260 K mostly result in low-level clouds and clear sky. At
<figure><img src="image_7.png" /><figcaption>FIG. 6. Conserved-variable ($\theta_e - r_T$) diagrams of the (a) current and (b) GPROF databases obtained from all the tropospheric levels.</figcaption></figure>

89 and 150 GHz, BTs from AMSU-B of less than 250 K are found over eastern Slovakia and on a line going from eastern Germany to Croatia. These depressed BTs result from significant scattering by large rimed ice particles embedded in the clouds. Note also that snow at the surface yields lower 89- and 150-GHz BTs over the Alps.

The Méso-NH simulation coupled with the radiative transfer codes captures the overall situation as seen in the 11-µm channel well, with high- and midlevel clouds at the right locations. This indicates that the model captures the overall atmospheric circulation. Depressed BTs for the 89- and 150-GHz channels are also simulated correctly over central Europe, but to a smaller

extent. The system over eastern Slovakia is almost missing. At 89 and 150 GHz, the surface signature of the cloud-free areas is correctly estimated by the surface climatology over snow and correctly modeled over sea. From the maps for other cases (not shown), similar conclusions can be drawn. The location of the cloud cover as revealed by the 11-µm channel is generally well predicted. The precipitating areas that lead to depressed BTs for the 89- and 150-GHz channels, while less predictable than an extensive cloud cover, present realistic scattered patterns at correct locations.

b. *Comparison of BT distribution*

The BT comparisons are summarized on BT histograms separated into land and sea surface conditions (Fig. 8). Over land, the grid points at altitudes higher than 1500 m were excluded to filter out the potential presence of snow at the surface. The grid points in the vicinity of coasts were also discarded to avoid large differences due to the contrast of the land-sea surface emissivity in the microwave region. The same flags for land, sea, and coast were applied for both the simulations and the observations. Note also that the satellite BTs at 11-µm (Meteosat) and 150 and 89 GHz (AMSU-B) result from a variable viewing angle while the 37-GHz channel (SSM/I) has a constant viewing angle. Only the vertical polarization of the 37-GHz channel is shown. The simulations are considered for incidence angles corresponding to the satellite observations.

Whatever the case and surface conditions, the distributions of observed BTs at 11-µm are continuously spread over the 200- and 280-K temperature range (Fig. 8). Two preferential modes are sometimes detectable (e.g., RHINE case) at low and high BTs. They are associated with high-level thick cloud and extended clear sky conditions, respectively. At 150 GHz, the observed distributions are highly skewed, leading to peak values between 260 and 280 K over land and to reduced BTs with a shift of 10-20 K over sea. A leading edge of minimum BT is also found, with fewer grid points for the light rain cases (RHINE and HOEK). At lower frequencies (89 and 37V GHz), the distributions of observed BTs are also unimodal over land, but with fewer grid points with low BT values. In contrast, over sea, the radiatively cold surface results in BT distributions peaking around 190-210 K. Emission by the hydrometeors explains the presence of some large values of BTs that widen the distributions.

Overall, the simulations reproduce well the shape of the BT distributions for all the channels explored (Fig. 8). The agreement is better over the ocean. Over land, some discrepancies can be seen from case to case. For instance, not enough low BTs are simulated at 150 GHz
<figure><img src="image_8.png" /><figcaption>Fig. 7. (top) Observed and (bottom) simulated BT (K) for the (left) 11-µm and the (middle) 150- and (right) 89-GHz channels at 0700 UTC 12 Aug 2002 (ELBE case).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>FIG. 8. Observed and simulated BT distributions for the 11-µm and 150-, 89- and 37V-GHz channels, separated into land and sea grid points for each case. The bin width is 5 K. The BTs are shifted to the right by 0, 100, 200, 300, 400, and 500 K for the RHINE, HOEK, ELBE, ALGER, and UKMIL cases, respectively.</figcaption></figure>

for the ELBE and HOEK cases, whereas the opposite is true for the UKMIL case. At 89 GHz, too many low BTs are simulated for the ELBE and UKMIL cases. This excess of depressed BTs at both frequencies for the UKMIL case suggests an excess of scattering by ice in the simulation. On the other hand, the variation of the discrepancies according to the frequencies for the ELBE case can be attributed to an incorrect representation of the hydrometeors in the meteorological model or to a misinterpretation of their scattering properties in the radiative transfer model.

The realism of the simulated BTs is further demonstrated by the joint BT distributions shown for selected pairs of channels for the observed and simulated data (Fig. 9). For AMSU-B frequencies (Fig. 9, top), the BTs at 90 and 150 GHz over land are distributed along the upper left of the diagonal, with less variability for the simulated BTs than for the observed BTs, at 90 GHz.

The BT depression at 150 GHz can be used as the primary parameter for the retrieval of the ice water path (Liu and Curry 1996). The observed relationship between the 37V- and 85V-GHz SSM/I channels (Fig. 9, middle) is also achieved by the simulations over both land and sea. However, BT simulations are also too low at 85V GHz; this is due to a few convective cells from the ALGER case (see also Fig. 8). Finally, joint BT distribution of horizontal versus vertical polarization for 37- and 85-GHz SSM/I channels are shown over sea (Fig. 9, bottom). Such a combination of polarizations at 37 and 85 GHz can be used to minimize temperature and surface water effects on the rain-rate retrieval (Conner and Petty 1998). The water surface emission is characterized by low and strongly polarized BT, while the effect of precipitation tends to increase BTs and to weaken the polarization difference. This appears to be well reproduced by the simulations at 37 GHz. At 85
<figure><img src="image_10.png" /><figcaption>FIG. 9. Observed and simulated joint BT distributions for (top) the 90- and 150-GHz AMSU channels, (middle) the 37V- and 85V-GHz channels, separated into land and sea grid points, and (bottom) the 37V- and 37H-GHz and the 85V- and 85H-GHz channels for grid points over sea only. The bin width is 5 K.</figcaption></figure>

GHz, the large depression caused by frozen hydrometeors yields a weak polarization difference for low BT values. This signal was not observed because of the lower resolution of the satellite; therefore, such anomalous BTs (from the ALGER case) might be withdrawn from the database for retrieval purposes. This shows that convective cases are specific challenges that require further analyses as well as more cases to be investigated.

### c. Verification of cloud cover and rain forecasts

A further step in the validation is made by the verification of cloud cover and rain forecasts. Here we use

categorical scores that measure the correspondence between simulated and observed occurrences of events at grid points. These scores were first developed to focus on tornado detection and later to verify the occurrence of high precipitation rates (Wilks 1995). In the following, we use the probability of detection (POD), the false-alarm ratio (FAR), the probability of false detection (POFD), and the Heidke skill score (HSS). The POD gives the relative number of times an event was forecast when it occurred, the FAR gives the relative number of times the event was forecast when it did not occur, the POFD is the fraction of no events that were incorrectly forecast as yes, and the HSS measures the
<figure><img src="image_11.png" /><figcaption>FIG. 10. Time evolution of (a) the POD (thick lines), the FAR (thin lines), and (b) the HSS of the high-cloud category (11-µm BT < 260 K) calculated for the RHINE, HOEK, and ELBE cases. The gray (black) lines represent the calculation grid point by grid point (area by area).</figcaption></figure>

fraction of correct forecasts after eliminating those that would be correct by chance. Such scores quantify the ability of the model to forecast an event at the right place.

The calculation of scores was first applied to the 11-µm Meteosat channel, taking advantage of the high temporal resolution of the observations. A threshold of 260 K was chosen to discriminate high- and midlevel thick clouds. The 24-h evolution of the POD and the FAR is shown for the RHINE, HOEK, and ELBE cases (Fig. 10). The comparison is made grid point by grid point (gray lines) and area by area (black lines). The calculation area by area compares fractions of the occurrence of events over a sized area (Roberts 2005). Such calculation takes the double penalty effect into account. The latter arises when an observed small-scale feature is more realistically forecast but is misplaced. Compared to a low-resolution model, a high-resolution model is penalized twice, once for missing the actual feature and again for forecasting it where it is not. The area used here is a square of five by five grid points (i.e., areas of 50 km by 50 km that exceed 50% of cloud cover).

For the three cases, when calculated grid point by grid point, the POD is generally over 0.5, the FAR is less than 0.5, and the HSS is positive, generally over 0.4.

This implies that the simulations have forecasting skill. Overall, the RHINE case gives the best forecast with the largest POD, (almost) the smallest FAR, and the largest HSS (at least after 15 h). This was to be expected as the cloud cover of a midlatitude front is the signature of well-predicted synoptic scales, whereas the two other cases concern two less well-organized cloud fields. This is further shown by the results of scores calculated area by area. For the RHINE case, the POD, the FAR, and the HSS comparing areas have the same high-skill values as the scores comparing grid points. In contrast, for the HOEK and ELBE cases, the POD, FAR, and HSS present a significant improvement. This indicates the good skill of the model to forecast the cloud cover on a 50-km scale.

Another application of the scores is to evaluate the skill of the model to detect rainfall over land. Algorithms for the detection of rain over land are usually based on the scattering signal of millimeter-size ice hydrometeors (e.g., Ferraro et al. 2000; Bennartz et al. 2002). To take advantage of the AMSU-B spatial resolution, we calculated the brightness temperature difference (BTD) between 89- and 150-GHz channels, albeit that both were affected by scattering (in contrast to the common combination of 23 and 89 GHz). The distribution of the rain rate with the BTD for the five simulations is shown in Fig. 11. As discussed by Bennartz et al. (2002), a larger probability of rainfall comes with a larger BTD.

The categorical scores can take this uncertainty into account. A relative operating characteristic (ROC) diagram plots the POD against the POFD using a set of increasing probability thresholds (for BTD decreasing from 4 to -4 K; Fig. 12). The comparison is made here pixel by pixel. The diagonal line means no skill at all, while the better the classifier, the closer the curve moves to the top left-hand corner (high POD with a low POFD). Almost all the points are in the top left-hand quadrant. This demonstrates the skill of all the simulations to detect BTD events, which by extension means the occurrence of rain events.

The rain forecasts were verified against 24-h accumulated rainfall measured by rain gauges for the 24-h simulations (RHINE, HOEK, and ELBE). Note that there is a 6-h shift between the 24-h accumulated rainfall measured at 0600 UTC by the rain gauges and those simulated at 0000 UTC from the model. The bias range from -12 to -1 mm (or between 20% and 30% in terms of relative bias) and the correlation coefficient are around 0.8 for the ELBE case and around 0.5 for the traveling front cases, the lowest correlation coefficient that can be partly explained by the 6-h shift. These statistics are comparable to those obtained for rain
<figure><img src="image_12.png" /><figcaption>FIG. 11. Distribution of rain rate according to the BTD between the 90- and 150-GHz channels over land.</figcaption></figure>

forecast over the Alps (Richard et al. 2007). When comparing categories of accumulated rainfall larger than 1 mm, POD is around 0.85 and FAR is around 0. The HSS is around 0.5, which shows the useful skill of the model in forecasting rainfall at the right place.

## 6. Conclusions

A cloud database of midlatitude situations has been presented. The meteorological cases are typical of the meteorological variability at midlatitudes. They not only include heavy rain episodes resulting in dramatic floods but also light precipitation events. They were selected over southern and northern parts of Europe during summer, autumn, and winter seasons. The distribution of the averaged vertical profiles of hydrometeors varies mostly with the 0°C isotherm, located on average between 1 and 3 km in height. The database also contains profiles where graupel and snow reach the ground. It thus differs significantly from the GPROF tropical database characterized by a 0°C isotherm located around 4.5 km in height. As a result, this database can complement the GPROF base for midlatitude situations. (The present midlatitude cloud database is available upon request from the first author.)

An evaluation of the simulations has been performed using satellite observations in both the thermal infrared and microwave regions through a model-to-satellite approach. The comparison is performed on a 10-km grid, which compares with the satellite spatial resolution. Whatever the channels, the observed and simulated BT distributions agree reasonably well for all the cases. As shown by Mech et al. (2007) and Defer et al. (2008), the simulations (the model outputs coupled with the radiative transfer codes) are realistic enough to be used as a cloud database for retrieval purposes.

Then the model-to-satellite approach is combined with the calculation of categorical scores. This allows the prediction of cloud and precipitation occurrences to be checked against satellite observations. In the infrared region, the Méso-NH model shows good skill in forecasting cloud cover. In particular, the frontal case (RHINE) displays higher POD and HSS and lower FAR than the other two cases investigated. This suggests better skill in forecasting synoptic-scale cloud systems. In the microwave region, a current diagnosis based on BTD between 89- and 150-GHz channels is used for rain detection. Despite the nonlinear relationship between BTD and rain, the simulations display skill in BTD categories with a varying threshold. In

<figure><img src="image_13.png" /><figcaption>FIG. 12. ROC diagram for BTD between the 90- and 150-GHz channels over land.</figcaption></figure>
the future, such diagnostic tools could be used in NWP models to verify the forecasts of cloud cover and rain all over the globe. Such a tool that monitors the performance for the cloud scheme in operational systems would be precious for further developing cloud schemes.

The current database provides physically consistent profiles of cloud, rain, pristine ice, snow, and graupel to be used as input to develop rain-rate retrieval methods over the midlatitudes. The statistical relationship between cloud and rain profiles and the surface rain rate shows that such an approach can be very challenging when based on satellite measurements that are essentially sensitive to the upper cloud layers. Using the current database, Mech et al. (2007) have shown the ability to retrieve integrated frozen hydrometeor contents with good accuracy, depending on the case. The current database can also be employed for exploring the capability of a submillimeter instrument as reported by Mech et al. (2007) and Defer et al. (2008). In the near future, it is planned to add other fully documented case studies to the database. In addition, the evaluation efforts will continue using active instruments like space-borne lidar and radar. These new instruments are well suited to testing the vertical hydrometeor distribution simulated by the Méso-NH model with more accuracy.

*Acknowledgments.* We thank Chris Kummerow for making the GPROF database available to us and Peter Bechtold for providing us with the rain gauge data. This study was supported by EUMETSAT under contract EUM/CO/04/1311/KJG and by ESA under contract 18054/04/NL/FF. Additional support for Eric Defer came from CNES under TOSCA contract "Etude mission pour la détection et le suivi des nuages de glace dans le domaine submillimétrique." Nathalie Söhne was supported by a CNES/Météo-France grant. Computer resources were allocated by IDRIS. AMSU data came from the NOAA Satellite Active Archive. METEOSAT observations are copyright 2003 EUMETSAT. The comments of the anonymous reviewers helped us to improve the presentation of the results.

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