Toward understanding of differences in current cloud retrievals of ARM ground-based measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes

Mesure du contenu en eau et en glace des nuages en phase mixte par radars multifréquences

TOULOUSE3-BU Sciences (315552104) / SudocTOULOUSE-Observ. Midi Pyréné (315552299) / SudocSudocFranceF

Cloud liquid water and ice content retrieval by multiwavelength radar

Cloud liquid water and ice content retrieval in precipitating clouds by the differential attenuation method using a dual-wavelength radar, as a function of the wavelength pair, is first discussed. In the presence of non-Rayleigh scatterers, drizzle, or large ice crystals, an ambiguity appears between attenuation and non-Rayleigh scattering. The liquid water estimate is thus biased regardless of which pair is used. A new method using three wavelengths (long λl, medium λm, and short λs) is then proposed in order to overcome this ambiguity. Two dual-wavelength pairs, (λl, λm) and (λl, λs), are considered. With the (λl, λm) pair, ignoring the attenuation, a first estimate of the scattering term is computed. This scattering term is used with the (λl, λs) pair to obtain an estimate of the attenuation term. With the attenuation term and the (λl, λm) pair, a new estimate of the scattering term is computed, and so on until obtaining a stable result. The behavior of this method is analyzed through a numerical simulation and the processing of field data from 3-, 35-, and 94-GHz radars

Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

International audienc

Stratocumulus liquid water content from dual-wavelength radar

Abstract A technique is described to retrieve stratocumulus liquid water content (LWC) using the differential attenuation measured by vertically pointing radars at 35 and 94 GHz. Millimeter-wave attenuation is proportional to LWC and increases with frequency, so LWC can be derived without the need to make any assumptions on the nature of the droplet size distribution. There is also no need for the radars to be well calibrated. A significant advantage over many radar techniques in stratocumulus is that the presence of drizzle drops (those with a diameter larger than around 50 μm) does not affect the retrieval, even though such drops may dominate the radar signal. It is important, however, that there are not significant numbers of drops larger than 600 μm, which scatter outside of the Rayleigh regime at 94 GHz. A lidar ceilometer is used to locate the cloud base in the presence of drizzle falling below the cloud. An accuracy of around 0.04 g m−3 is achievable with averaging over 1 min and 150 m (two range gates), but for the previously suggested frequency pair of 10 and 35 GHz, the corresponding accuracy would be considerably worse at 0.34 g m−3. First, the retrieval of LWC is simulated using aircraft-measured size spectra taken from a profile through marine stratocumulus. Results are then presented from two case studies—one using two cloud radars at Chilbolton in southern United Kingdom, and another using the Cloud Profiling Radar System at the Atmospheric Radiation Measurement site in Oklahoma. The liquid water path from the technique was found to be in good agreement with the values that were obtained from microwave radiometers, with the difference between the two being close to the accuracy of the radiometer retrieval. In the case of well-mixed stratocumulus, the profiles were close to adiabatic.</jats:p

Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

International audienceThe extrapolation of the precipitation to the ground from radar reflectivities measured at the beam altitude is one of the most delicate phases of radar data processing for producing quantitative precipitation estimations (QPEs) and remains a major scientific issue. In many operational meteorological services such as Météo-France, a vertical profile of reflectivity (VPR) correction is uniformly applied over a large part or the entire radar domain. This method is computationally efficient, and the overall bias induced by the bright band is most of the time well corrected. However, this way of proceeding is questionable in situations with high spatial and vertical variability of precipitation (during the passage of a cold front or in a complex terrain, for example). This study initiates from two statements: first, radars provide information on precipitation with a high spatio-temporal resolution but still require VPR corrections to extrapolate rain rates at the ground level. Second, the horizontal resolution of some numerical weather prediction (NWP) models is now comparable with the radar one, and their dynamical core and microphysics schemes allow the production of realistic simulations of VPRs. The present paper proposes a new approach to assess surface rainfall from radar reflectivity aloft by exploiting simulated VPRs and rainfall forecasts from the high-resolution NWP model AROME-NWC. To our knowledge, this is the first time that simulated precipitation profiles from an NWP model are used to derive radar QPEs. The implementation of the new method on two stratiform situations provided significant improvements on the hourly and 6 h accumulations compared to the operational QPEs, showing the relevance of this new approach

Preliminary investigation of the relationship between differential phase shift and path-integrated attenuation at the X band frequency in an Alpine environment

International audienceThe RadAlp experiment aims at developing advanced methods for rainfall and snowfall estimation using weather radar remote sensing techniques in high mountain regions for improved water resource assessment and hydrological risk mitigation. A unique observation system has been deployed since 2016 in the Grenoble region of France. It is composed of an X-band radar operated by Météo-France on top of the Moucherotte mountain (1901 m  above sea level; hereinafter MOUC radar). In the Grenoble valley (220 m  above sea level; hereinafter a.s.l.), we operate a research X-band radar called XPORT and in situ sensors (weather station, rain gauge and disdrometer). In this paper we present a methodology for studying the relationship between the differential phase shift due to propagation in precipitation (Φdp) and path-integrated attenuation (PIA) at X band. This relationship is critical for quantitative precipitation estimation (QPE) based on polarimetry due to severe attenuation effects in rain at the considered frequency. Furthermore, this relationship is still poorly documented in the melting layer (ML) due to the complexity of the hydrometeors' distributions in terms of size, shape and density. The available observation system offers promising features to improve this understanding and to subsequently better process the radar observations in the ML. We use the mountain reference technique (MRT) for direct PIA estimations associated with the decrease in returns from mountain targets during precipitation events. The polarimetric PIA estimations are based on the regularization of the profiles of the total differential phase shift (Ψdp) from which the profiles of the specific differential phase shift on propagation (Kdp) are derived. This is followed by the application of relationships between the specific attenuation (k) and the specific differential phase shift. Such k–Kdp relationships are estimated for rain by using drop size distribution (DSD) measurements available at ground level. Two sets of precipitation events are considered in this preliminary study, namely (i) nine convective cases with high rain rates which allow us to study the ϕdp–PIA relationship in rain, and (ii) a stratiform case with moderate rain rates, for which the melting layer (ML) rose up from about 1000 up to 2500 m a.s.l., where we were able to perform a horizontal scanning of the ML with the MOUC radar and a detailed analysis of the ϕdp–PIA relationship in the various layers of the ML. A common methodology was developed for the two configurations with some specific parameterizations. The various sources of error affecting the two PIA estimators are discussed, namely the stability of the dry weather mountain reference targets, radome attenuation, noise of the total differential phase shift profiles, contamination due to the differential phase shift on backscatter and relevance of the k–Kdp relationship derived from DSD measurements, etc. In the end, the rain case study indicates that the relationship between MRT-derived PIAs and polarimetry-derived PIAs presents an overall coherence but quite a considerable dispersion (explained variance of 0.77). Interestingly, the nonlinear k–Kdp relationship derived from independent DSD measurements yields almost unbiased PIA estimates. For the stratiform case, clear signatures of the MRT-derived PIAs, the corresponding ϕdp value and their ratio are evidenced within the ML. In particular, the averaged PIA∕ϕdp ratio, a proxy for the slope of a linear k–Kdp relationship in the ML, peaks at the level of the copolar correlation coefficient (ρhv) peak, just below the reflectivity peak, with a value of about 0.42 dB per degree. Its value in rain below the ML is 0.33 dB per degree, which is in rather good agreement with the slope of the linear k–Kdp relationship derived from DSD measurements at ground level. The PIA∕ϕdp ratio remains quite high in the upper part of the ML, between 0.32 and 0.38 dB per degree, before tending towards 0 above the ML