60 research outputs found
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A comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS
The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radarâlidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and CloudâAerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function
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The representation of the West-African Monsoon vertical cloud structure in the Met Office Unified Model: an evaluation with CloudSat
Weather and climate model simulations of the West African Monsoon (WAM) have generally poor representation of the rainfall distribution and monsoon circulation because key processes, such as clouds and convection, are poorly characterized. The vertical distribution of cloud and precipitation during the WAM are evaluated in Met Office Unified Model simulations against CloudSat observations. Simulations were run at 40-km and 12-km horizontal grid length using a convection parameterization scheme and at 12-km, 4-km, and 1.5-km grid length with the convection scheme effectively switched off, to study the impact of model resolution and convection parameterization scheme on the organisation of tropical convection. Radar reflectivity is forward-modelled from the model cloud fields using the CloudSat simulator to present a like-with-like comparison with the CloudSat radar observations. The representation of cloud and precipitation at 12-km horizontal grid length improves dramatically when the convection parameterization is switched off, primarily because of a reduction in daytime (moist) convection. Further improvement is obtained when reducing model grid length to 4 km or 1.5 km, especially in the representation of thin anvil and mid-level cloud, but three issues remain in all model configurations. Firstly, all simulations underestimate the fraction of anvils with cloud top height above 12 km, which can be attributed to too low ice water contents in the model compared to satellite retrievals. Secondly, the model consistently detrains mid-level cloud too close to the freezing level, compared to higher altitudes in CloudSat observations. Finally, there is too much low-level cloud cover in all simulations and this bias was not improved when adjusting the rainfall parameters in the microphysics scheme. To improve model simulations of the WAM, more detailed and in-situ observations of the dynamics and microphysics targeting these non-precipitating cloud types are required
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Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations
Turbulent mixing processes are important in determining the evolution of convective clouds,and the production of convective precipitation. However, the exact nature of these impacts remains uncertain due to limited observations. Model simulations show that assumptions made in parametrizing turbulence can have a marked effect on the characteristics of simulated clouds. This leads to significant uncertainty in forecasts from convectionâpermitting numerical weather prediction (NWP) models. This contribution presents a comprehensive method to retrieve turbulence using Doppler weather radar to investigate turbulence in observed clouds. This method involves isolating the turbulent component of the Doppler velocity spectrum width, expressing turbulence intensity as an eddy dissipation rate, Ï”. By applying this method throughout large datasets of observations collected over the southern United Kingdom using the (0.28° beamâwidth) Chilbolton Advanced Meteorological Radar (CAMRa), statistics of convective cloud turbulence are presented. Two contrasting case days are examined: a shallow âshowerâ case, and a âdeep convectionâ case, exhibiting stronger and deeper updraughts. In our observations, Ï” generally ranges from 10â3 to 10â1 m2/s3, with the largest values found within, around and above convective updraughts. Vertical profiles of Ï” suggest that turbulence is much stronger in deep convection; 95th percentile values increase with height from 0.03 to 0.1 m2/s3, compared to approximately constant values of 0.02â0.03âm2/s3 throughout the depth of shower cloud. In updraught regions on both days, the 95th percentile of Ï” has significant (pâ< 10â3) positive correlations with the updraught velocity, and the horizontal shear in the updraught velocity, with weaker positive correlations with updraught dimensions. The Ï”âretrieval method presented considers a very broad range of conditions, providing a reliable framework for turbulence retrieval using highâresolution Doppler weather radar. In applying this method across many observations, the derived turbulence statistics will form the basis for evaluating the parametrization of turbulence in NWP models
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Convective initiation and storm lifeâcycles in convectionâpermitting simulations of the Met Office Unified Model over South Africa
Convective initiation is a challenge for convectionâpermitting models due to its sensitivity to subâkm processes. We evaluate the representation of convective storms and their initiation over South Africa during four summer months in Met Office Unified Model simulations at 1.5âkm horizontal grid length. Storm size distributions from the model compare well against radar observations, but rainfall in the model is predominantly produced by large storms (50 km in diameter or larger) in the evening, whereas radar observations show most rainfall occurs throughout the afternoon, from storms 10â50 km in diameter. In all months, modelled maximum number of storm initiations occurs at least 2 hours prior to the radarâobserved maximum. However, the diurnal cycle of rainfall compares well between model and observations, suggesting the numerous storm initiations in the simulations do not produce much rainfall. Modelled storms are generally less intense than in the radar observations, especially in early summer. In February, when tropical influences dominate, the simulated storms are of similar intensity to observed storms. Simulated storms tend to reach their peak intensity in the first 15 minutes after initiation, then gradually become less intense as they grow. In radar observations, storms reach their peak intensity 15â30 minutes into their life cycle, stay intense as they grow larger, then gradually weaken after they have reached their maximum diameter. Two November case studies of severe convection are analysed in detail. Higher resolution grid length initiates convection slightly earlier (300 m cf. 1.5 km) with the same science settings. Two 1.5âkm simulations that apply more subâgrid mixing have delayed convective initiation. Analysis of soundings indicates little difference in convective indices, suggesting that differences in convection may be attributed to choices in subâgrid mixing parameters
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Assessment of the representation of West African storm lifecycles in convectionâpermitting simulations
Convectionâpermitting models perform better at representing the diurnal cycle and the intermittency of convective rainfall over land than parameterizedâconvection models. However, most of the previous model assessments have been from an Eulerian point of view, while key impacts of the rainfall depend on a stormârelative perspective of the system lifecycle. Here a stormâtracking algorithm is used to generate stormâcentered Lagrangian lifecycle statistics of precipitation over West Africa from regional climate model simulations and observations. Two versions of the Met Office Unified Model with and without convection parameterization at 4, 12, and 25 km resolution were analyzed. In both of the parameterizedâconvection simulations, storm lifetimes are too short compared to observations, and storms have no preferred propagation direction; the diurnal cycle of initiations and dissipations and the spatial distribution of storms are also inaccurate. The storms in the convectionâpermitting simulations have more realistic diurnal cycles and lifetimes, but are not as large as the largest observed storms. The convectionâpermitting model storms propagate in the correct direction, although not as fast as observed storms, and they have a much improved spatial distribution. The rainfall rate of convectionâpermitting storms is likely too intense compared to observations. The improved representation of the statistics of organized convective lifecycles shows that convectionâpermitting models provide better simulation of a number of aspects of highâimpact weather which are critical to climate impacts in this important geographic region, providing the high rainfall rates can be taken into account
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How skilful are nowcasting satellite applications facility products for tropical Africa?
Satellite nowcasting potentially provides a vital opportunity to mitigate against the risks of severe weather in tropical Africa, where population growth and climate change are exposing an ever growing number of people to weather hazards. Numerical weather prediction demonstrates limited skill for much of Africa and weather radars are rare. However, geostationary satellites provide excellent spatial and temporal coverage of the often longâlasting convective storms that deliver heavy rain, lightning and strong winds, presenting a valuable opportunity for satellite nowcasting. Here, we evaluate the skill of satellite nowcasting products for tropical Africa: these products are routinely generated, but to our best knowledge never routinely used in tropical Africa before the Global Challenges Research Fund African SWIFT (Science for Weather Information and Forecasting Techniques) project. Focusing in particular on convective rainfall rate (CRR) and rapidly developing thunderstorm convection warning (RDTâCW) products, we demonstrate that both are useful nowcasting tools. The CRR product produces very different rainfall climatologies for day and night in tropical Africa. This is associated with greater skill of the product during daytime, particularly for heavier rain rates. The RDTâCW product is able to identify around 60% of heavy (>5âmm·hrâ1) rainfall events with the fraction detected increasing with increasing rainfall rate. For both products, extrapolation forwards in time (up to 90 and 60âmin, respectively) maintains useful skill in tropical Africa, motivating work to develop longer leadâtime nowcasts. We conclude that widespread uptake of satellite nowcasting could provide new skilful weather predictions on short timeâscales in much of tropical Africa
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Do convection-permitting ensembles lead to more skilful short-range probabilistic rainfall forecasts over tropical East Africa?
Convection-permitting ensemble prediction systems (CP-ENS) have been implemented in the mid-latitudes for weather forecasting timescales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the UK Met Office over tropical East Africa, for 24 cases in the period April-May 2019. The CP-ENS, an ensemble with parametrized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skilful forecast for both 3-h and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighbourhood approach, show that the CP-ENS is skilful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the mid-latitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is under-spread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance
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Validation of GPM IMERG extreme precipitation in the Maritime Continent by station and radar data
The Maritime Continent (MC) is a region subject to high impact weather (HIW) events, which are still poorly predicted by numerical weather prediction (NWP) models. To improve predictability of such events, NWP need to be evaluated against accurate measures of extreme precipitation across the whole MC. With its global spatial coverage at high spatio-temporal resolution, the Global Precipitation Measurement (GPM) dataset is a suitable candidate. Here we evaluate extreme precipitation in the Integrated Multi-Satellite Retrieval for GPM (IMERG) V06B product against station data from the Global Historical Climatology Network (GHCN) in Malaysia and the Philippines. We find that the high intra-grid spatial variability of precipitation extremes results in large spatial sampling errors when each IMERG gridbox is compared with individual co-located precipitation measurements, a result that may explain discrepancies found in earlier studies in the MC. Overall, IMERG daily precipitation is similar to station precipitation between the 85th and 95th percentile, but tends to overestimate above the 95th. IMERG data were also compared with radar data in western Peninsular Malaysia for sub-daily timescales. Allowing for uncertainties in radar data, the analysis suggests that the 95th percentile is still suitable for NWP evaluation of extreme sub-daily precipitation, but that the rainfall rates diverge at higher percentiles. Hence, our overall recommendation is that the 95th percentile be used to evaluate NWP forecasts of HIW on daily and sub-daily time scales against IMERG data, but that higher percentiles (i.e., more extreme precipitation) be treated with caution
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