Surface Rain Rate Research Articles

Evaluating the Effects of Raindrop Motion on the Accuracy of the Precipitation Inversion Algorithm by X-SAR

Precipitation has a profound impact on both human life and the natural environment. X-band synthetic aperture radar (X-SAR) utilizes high-resolution microwave remote-sensing technology, providing opportunities for global precipitation measurements. The current precipitation inversion algorithms from X-SAR measurements assume that precipitation particles remain relatively stationary with the ground. However, the motion of raindrops could potentially reduce the accuracy of these algorithms. In this study, we first established a functional relationship between raindrop motion and SAR echoes based on the standard deviation of the raindrop Doppler velocity spectrum. Secondly, an exploratory algorithm was proposed to retrieve rainfall distribution under the raindrop motion error model (RMM) and quantitatively calculate the precipitation inversion error caused by raindrop motion. In comparison to conditions where the atmosphere is stationary, when the standard deviation of the Doppler velocity spectrum of raindrops is 1.1 m/s, the relative error of the retrieved surface rain rate increases from 2.1% to 35.8%. Numerical simulations show that SAR echoes are sensitive to changes in the standard deviation of the Doppler velocity spectrum, and the impact of raindrop motion on the accuracy of X-SAR precipitation measurements cannot be neglected.

Aerosol effects on the three-dimensional structure of organized precipitation systems over Beijing-Tianjin-Hebei region in summer

Aerosol effects on organized precipitation systems (PSs) over Beijing-Tianjin-Hebei (BTH) region are investigated using 6 years of Global Precipitation Measurement (GPM) observations. An auto-clustering algorithm was used to identify local-scale PSs under different aerosol loading conditions and to quantify their three-dimensional structure properties. The general characteristics of PSs show a non-monotonic trend with increasing aerosol optical depth (AOD). The near surface rain rate, the maximum radar reflectivity, the horizontal precipitation area and the vertical extension of PSs decrease with increasing AOD when aerosol loading is low. However, for higher aerosol loadings, the precipitation area first increases and then decreases with increasing AOD. By constraining the variations of meteorology conditions and by calculating the partial correlations, aerosol effects rather than meteorology effects are most likely the major causes for the observed changes of PSs. One possible explanation is that the changes of proportions of different aerosol types with increasing AOD have different mechanisms for affecting the macro-physical and micro-physical properties of PSs. The findings provide a valuable insight into how aerosols affect the three-dimensional structure of local-scale PSs over a region with complex aerosol composition.

EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset

Abstract. The European climatological high-resolution gauge-adjusted radar precipitation dataset, EURADCLIM, addresses the need for an accurate (sub)daily precipitation product covering 78 % of Europe at a high spatial resolution. A climatological dataset of 1 and 24 h precipitation accumulations on a 2 km grid is derived for the period 2013 through 2020. The starting point is the European Meteorological Network (EUMETNET) Operational Program on the Exchange of Weather Radar Information (OPERA) gridded radar dataset of 15 min instantaneous surface rain rates, which is based on data from, on average, 138 ground-based weather radars. First, methods are applied to further remove non-meteorological echoes from these composites by applying two statistical methods and a satellite-based cloud-type mask. Second, the radar composites are merged with the European Climate Assessment & Dataset (ECA&D) with potentially ∼ 7700 rain gauges from National Meteorological and Hydrological Services (NMHSs) in order to substantially improve its quality. Characteristics of the radar, rain gauge and satellite datasets are presented, as well as a detailed account of the applied algorithms. The clutter-removal algorithms are effective while removing few precipitation echoes. The usefulness of EURADCLIM for quantitative precipitation estimation (QPE) is confirmed by comparison against rain gauge accumulations employing scatter density plots, statistical metrics and a spatial verification. These show a strong improvement with respect to the original OPERA product. The potential of EURADCLIM to derive pan-European precipitation climatologies and to evaluate extreme precipitation events is shown. Specific attention is given to the remaining artifacts in and limitations of EURADCLIM. Finally, it is recommended to further improve EURADCLIM by applying algorithms to 3D instead of 2D radar data and by obtaining more rain gauge data for the radar–gauge merging. The EURADCLIM 1 and 24 h precipitation datasets are publicly available at https://doi.org/10.21944/7ypj-wn68 (Overeem et al., 2022a) and https://doi.org/10.21944/1a54-gg96 (Overeem et al., 2022b).

Has There Been a Recent Shallowing of Tropical Cyclones?

AbstractMany aspects of tropical cyclone (TC) properties at the surface have been changing but any systematic vertical changes are unknown. Here, we document a recent trend of high thick clouds of TCs. The global inner‐core high thick cloud fraction measured by satellite has decreased from 2002 to 2021 by about 10% per decade. The TC inner‐core surface rain rate is also found to have decreased during the same period by a similar percentage. This suppression of high thick clouds and rain has been largest during the intensification phase of the strongest TCs. Hence, these two independent and consistent observations suggest that the TC inner‐core convection has weakened and that TCs have become shallower recently at least. For this period, the lifetime maximum intensity of major TCs has not changed and this suggests an increased efficiency of the spin‐up of TCs.

How Accurately Can Warm Rain Realistically Be Retrieved with Satellite Sensors? Part II: Horizontal and Vertical Heterogeneities

Abstract There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architectures—one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%–50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% as compared with −28%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals.

Effects of anthropogenic aerosols and sea salt aerosols during a summer precipitation event in the Yangtze River Delta

Currently, we know little about the effects of anthropogenic aerosols (AAs) and sea salt aerosols (SSAs) on heavy precipitation over the densely populated coastal areas. In this view, we developed a local aerosol activation parametrization scheme which conforms to the Yangtze River Delta (YRD) characteristics using cloud condensation nucleus (CCN) with supersaturation at 0.4 (CCN0.4) and aerosol optical depth (AOD) data at 550 nm (AOD550) to improve the model performance. Meanwhile, we simulated a heavy precipitation over the YRD during early summer using the WRF-Chem model (version 4.1) with the Morrison microphysics scheme. Increased AAs and SSAs presented opposite responses in droplets concentrations and precipitation intensity. The increased AAs increased the aerosol concentrations, leading to more but smaller droplets; and the cloud droplets mixing ratio Qc was slightly increased by 17.7% due to the weakened auto-conversion from droplets to raindrops, while the rain mixing ratio Qr was decreased by about 5.7%. On the contrary, presence of SSAs increased the effective (EFC) radius of cloud droplets because of the larger particles. Along with increased AAs, due to the influences of decreased Qr and smaller droplets, probability density function (PDF) of the hourly surface rain rates shifted to the smaller values (< 0.5 mm/h), which was caused by lowered latent heat release resulting from the enhanced rain evaporative cooling and weakened condensation rate, weakening the convective intensity and updrafts. Meanwhile, decreased updrafts resulted in fewer cloud droplets being transformed to the upper air, less accretion of droplets by ice crystal and snow, and reduced ice mixing ratio Qi and snow mixing ratio Qs. The averaged surface precipitation accumulated for 24 h decreased by about 7.2%, and the intensity of radar reflectivity was decreased by about 7.0%. In contrast, presence of SSAs enhanced the auto-conversion droplets and weakened the evaporation of raindrops, thus increasing the Qr. In this case, the 24-h accumulated averaged surface rainfall was increased by about 30.1% with the larger radar reflectivity and increased area of large reflectivity values (> 20 dbz). The latent heat release and related microphysical processes were enhanced due to the changes of conversion rate, which, together with the increased Qr and larger droplets, strengthened the precipitation, increased the incidence of heavier precipitation (> 2 mm/h), and enhanced the ice-phase microphysical processes.

Climatology and characteristics of rapidly intensifying tropical cyclones over the North Indian Ocean

AbstractRapid intensification (RI) of a tropical cyclone (TC) is defined as an increase in the wind speed by 30 knots within 24 hours. It is a less understood process, particularly for TCs over the North Indian Ocean. We considered data for the past 39 years (1982–2020) from the JTWC and studied the climatology of TCs. We found that, out of 197 cases, 44 TCs (22%) experienced RI. There is a significant increasing trend in RI TCs frequency and duration over the Arabian Sea (AS). Lifetime maximum intensity (LMI) is significantly higher, and genesis to LMI duration is also significantly longer for TCs undergoing RI over the AS. LMI is double for RI TC than for non‐RI (NRI) TC. The longer duration to achieve the higher maximum intensity resulted in a longer total lifespan for RI TCs. All TCs reaching a super cyclonic storm category underwent RI and generally, RI onset happens in the depression stage. The composites are prepared to understand the characteristics of RI TCs in contrast to NRI TCs. For the RI TC composite, the period includes 12 hours before and 24 hours after the RI onset. For the NRI TC, we considered 36 hours starting from the initial intensification stage. The analysis shows the RI TCs are embedded in the warm water regions with ocean heat content of 90–100 kJ · cm−2 and strong latent heat flux transport. RI TCs also have higher positive low‐level relative vorticity, higher upper‐level divergence, and higher mid‐level relative humidity, with higher moisture content around the TC centre. Deep clouds with 208 K &lt; IRBT &lt;240 K dominate in RI TCs. The total surface rainfall for RI cases is symmetric around the centre. RI TCs have higher rainfall fractions with intense surface rain rates for stratiform and convective rainfall during the pre‐monsoon.

Large‐eddy simulations of drizzling shallow cumuli using a turbulence‐aware autoconversion parametrization

AbstractTurbulence in clouds enhances collision between cloud droplets, which is critical to drizzle formation in shallow cumulus clouds. In this study, we develop an autoconversion parametrization that considers the turbulence‐induced collision enhancement (TICE) obtained from a particle trajectory model. We implement the developed parametrization to a large‐eddy simulation model and simulate the Rain In Cumulus over the Ocean (RICO) case to investigate the effects of TICE on shallow cumulus clouds and their sensitivities to the cloud droplet number concentration and horizontal grid resolution. TICE significantly increases the autoconversion rate, leading to large increases in the accretion rate, rainwater path and surface rain rate. The simulations of shallow cumulus clouds are highly sensitive to the cloud droplet number concentration and horizontal grid resolution, and the inclusion of TICE overall reduces the sensitivities. For example, the large rainwater path and surface rain rate that appear when cloud droplet number concentration is relatively low are reduced by TICE, whereas the very small rainwater path and surface rain rate that appear when cloud droplet number concentration is relatively high are increased by TICE. Comparisons with two other TICE‐aware parametrizations based on direct numerical simulation results of droplet collisions in turbulence show that there are substantial differences in TICE effects for different parametrizations, which implies a large uncertainty in simulating turbulent clouds. Among the three parametrizations, the parametrization developed in this study shows intermediate TICE‐induced increases in rainwater path and surface rain rate.

Climatologies of Mesoscale Convective Systems over China Observed by Spaceborne Radars

Abstract This study investigates the characteristics of mesoscale convective systems (MCSs) as a function of MCS organizational mode over China, using long-term precipitation radar observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). The spaceborne radar-based MCS climatology shows maximum population over low-elevation regions, marked decrease over the foothills, and minimum frequency over the Tibetan Plateau. Linear and nonlinear MCSs account for 17% and 83% of the MCSs over China, respectively. Linear MCSs have much stronger convective intensity and heavier precipitation than nonlinear MCSs, as indicated by TRMM convective proxies. Interestingly, though broad-stratiform MCSs have the weakest convection, they produce the heaviest (maximum) rain rate and the largest amount of heavy rainfall among nonlinear MCSs. Among various types of linear MCSs, bow echoes (BEs) and no-stratiform (NS) systems exhibit the strongest convective intensity, embedded lines exhibit the weakest, and convective lines with trailing/leading stratiform in between. BEs and NSs share the most vertically extended structure, strongest microwave ice scattering, and highest lightning flash rates, but NS systems have a much lower surface rain rate likely due to a drier environment. Vertical radar reflectivity profiles suggest that both ice-based and warm-rain processes play an important role in the precipitation processes of linear MCSs over China, including the most intense BE storms. In short, this study helps to better understand the convective organization, precipitation structure, and ensemble microphysical properties of MCSs over China, and potentially provides guidelines for evaluating high-resolution model simulations and satellite rainfall retrievals for monsoonal MCSs.

Investigating the feasibility of using precipitation measurements from weather RaDAR to estimate potential recharge in regional aquifers: the Majella massif case study in Central Italy

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.

An Algorithm for Retrieving the 2-D Distribution of Moderate Rain by X-SAR

Synthetic aperture radar (SAR) can detect ground information with high precision, which provides another opportunity for the retrieval of rain. Rainfall intensities in East Asia are mainly moderate. The current retrieval algorithms have high accuracy in rainstorms, but they overestimate the rainfall intensity greatly in moderate rain. Therefore, it is very important to reduce the retrieval error of SAR in moderate rain. After analyzing the scattering model of precipitation, this paper proposes an algorithm for retrieving 2-D moderate rain distribution (MRA). Since the 2-D distribution of rain is related to the vertical and horizontal distributions, MRA combines the empirical regression equation with the directional model of rain rates at different levels to retrieve the vertical distribution of precipitation. Compared with the model-oriented statistical (MOS) algorithm, MRA reduces the root mean square error when retrieving the surface rain rate from 2.6 to 0.1. In addition, based on the high-precision rain parameters retrieved by MRA, the horizontal distribution is retrieved through the likelihood distance. This horizontal distribution retrieval method not only has less amount of calculation but also avoids the difficulties of mathematical analysis.

The Influence of the Diurnal Cycle in Wind Shear and Thermodynamics on Squall Lines in the West African Monsoon

Abstract The West African monsoon has a clear diurnal cycle in boundary layer properties, synoptic flow, and moist convection. A nocturnal low-level jet (LLJ) brings cool, moist air into the continent and we hypothesize that it may support storms by providing vertical wind shear and a source of moisture. We use idealized simulations to investigate how the mean diurnal cycle in temperature and humidity compared with that of the wind shear impacts on mature squall lines. Thermodynamic diurnal changes dominate those of the winds, although when isolated the LLJ wind is favorable for more intense systems. Bulk characteristics of the storms, including in-cloud upward mass flux and—if precipitation evaporation is accounted for—total surface rain rates, correlate well with the system-relative inflow of convectively unstable air and moisture into the storms. Mean updraft speeds and mean rainfall rates over the storms do not correlate as well with system-relative inflows due to variations in storm morphology such as cold pool intensity. We note that storms tend to move near the speed of the African easterly jet and so maximize the inflow of convectively unstable air. Our results explain the observed diurnal cycle in organized moist convection, with the hours from 1800 to 0000 UTC being the most favorable. Storms are more likely to die after this, despite the LLJ supporting them, with the environment becoming more favorable again by midday. Significance Statement Large organized storms dominate rainfall in the West African Sahel, but models struggle to predict them at the correct time of day and the underlying mechanisms that control their timings are not well understood. Using idealized simulations, we show that the temperature and humidity of the late evening are favorable for such storms whereas inflow from the low-level jet supports storms overnight. Storm inflows of available energy and moisture predict upward mass transport and total rainfall rates, whereas the strength of the storm’s cold pool is important for storm structure and intensity. Our results demonstrate how the environmental wind profile (which varies throughout the day) interacts with internal storm dynamics, posing a major challenge to parameterized models.

Characteristics of clouds, precipitation, and latent heat in midlatitude frontal system mixed with dust storm from GPM satellite observations and WRF simulations

A heavy dust storm originating in Mongolia and Inner Mongolia traveled to Northeast China and met a midlatitude frontal system on May 3, 2017. The potential ice nuclei (IN) effects of mineral dust aerosols on the vertical structure of clouds, precipitation, and latent heat (LH) were studied using Global Precipitation Mission (GPM) satellite observations and Weather Research and Forecasting (WRF) model simulations. The WRF simulations correctly captured the main features of the system, and the surface rain rate distribution was positively correlated with data retrieved from the GPM Microwave Imager. Moreover, the correlation coefficient increased from 0.31 to 0.54 with increasing moving average window size. The WRF-simulated rainfall vertical profiles are generally comparable to the GPM Dual-Frequency Precipitation Radar (DPR) observations, particularly in low layers. The joint probability distribution functions of the rain rate at different altitudes from the WRF simulation and GPM observations show high positive correlation coefficients of ~0.80, indicating that the assumptions regarding the raindrop size distribution in the WRF model and DPR retrieval were consistent. Atmospheric circulation analysis and aerosol optical depth observations from the Himawari-8 satellite indicated that the dust storm entered only a narrow strip of the northwest edge of the frontal precipitation system. The WRF simulations showed that in carefully selected areas of heavy dust, dust can enhance the heterogeneous ice nucleation process and increase the cloud ice, snowfall, high-altitude precipitation rate, and LH rate in the upper layers. This effect is significant at temperatures of −15 °C to −38 °C and requires dust number concentrations exceeding 10&lt;sup&gt;6&lt;/sup&gt; m&lt;sup&gt;−&lt;/sup&gt;&lt;sup&gt;3&lt;/sup&gt;. It is important to accurately classify the dusty region in this type of case study. In the selected vertical cross section, the WRF-simulated and DPR-retrieved LH have comparable vertical shapes and amplitudes. Both results reflect the structure of the tilted frontal surface, with positive LH above it and negative LH below it. The simulated area-averaged LH profiles show positive heating in the entire column, which is a convective-dominated region, and this feature is not significantly affected by dust. DPR-based LH profiles show stratiform-dominated or convective-dominated shapes, depending on the DPR retrieval product.

Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.

Evaluation of Microphysics Schemes in Tropical Cyclones Using Polarimetric Radar Observations: Convective Precipitation in an Outer Rainband

AbstractCloud microphysics significantly impact tropical cyclone precipitation. A prior polarimetric radar observational study by the authors revealed the ice-phase microphysical processes as the dominant microphysics mechanisms responsible for the heavy precipitation in the outer rainband of Typhoon Nida (2016). To assess the model performance regarding microphysics, three double-moment microphysics schemes (i.e., Thompson, Morrison, and WDM6) are evaluated by performing a set of simulations of the same case. While these simulations capture the outer rainband’s general structure, microphysics in the outer rainbands are strikingly different from the observations. This discrepancy is primarily attributed to different microphysics parameterizations in these schemes, rather than the differences in large-scale environments due to cloud–environment interactions. An interesting finding in this study is that the surface rain rate or liquid water content is inversely proportional to the simulated mean raindrop sizes. The mass-weighted raindrop diameters are overestimated in the Morrison and Thompson schemes and underestimated in the WDM6 scheme, while the former two schemes produce lower liquid water content than WDM6. Compared with the observed ice water content based on a new polarimetric radar retrieval method, the ice water content above the environmental 0°C level in all simulations is highly underestimated, especially at heights above 12 km MSL where large concentrations of small ice particles are typically prevalent. This finding suggests that the improper treatment of ice-phase processes is potentially an important error source in these microphysics schemes. Another error source identified in the WDM6 scheme is overactive warm-rain processes that produce excessive concentrations of smaller raindrops.

Estimation and evaluation of rainfall from INSAT-3D improved IMSRA algorithm during 2018 summer monsoon season

Indian National Satellite System (INSAT) multi-spectral Rainfall Algorithm (IMSRA) is an operational rainfall estimation technique of INSAT-3D at Indian Meteorological Department (IMD) and Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) since 2014. The IMSRA rain estimates are widely used for various application studies. This algorithm is based on an empirical power-law relation between infrared brightness temperature and surface rain rate. Based on further researches, some more innovative elements are recognized and incorporated into the parent IMSRA algorithm, termed as improved IMSRA. The present study evaluates the potential of the two versions of IMSRA algorithms for estimation of accumulated rainfall and its comparison with Global Precipitation Measurement (GPM) based IMERG rainfall during the 2018 summer monsoon period. Studies suggest satellite rainfall data at monthly and seasonal scales are better suited for hydrological and meteorological applications. Much studies are not done towards the evaluation and characterizations of the two versions of IMSRA algorithms at monthly and seasonal scales, and the present study focuses on such comparisons. The results indicate that the overestimation tendencies of the older version of the IMSRA algorithm are reduced in the improved version. The statistical analysis suggests that the updated version show a reduction ~25% error for the improved version in the seasonal estimation of rainfall.

Precipitation structure during various phases the life cycle of precipitating cloud systems using geostationary satellite and space-based precipitation radar over Peru

ABSTRACT The life cycle of clouds consists of mainly into three phases, namely developing, mature, and dissipating phases. The information about the vertical structure of the precipitation during different phases of development will improve their representation in the cloud models. Whether specific regimes over Peru favor the formation or decay of the cloud systems and how their intensity varies during different phases of development will provide the insight into the precipitation structure over Peru. We used two satellite-based data, namely from Global Precipitation Measurement dual Precipitation Radar (GPM-DPR) and GOES (Geostationary Operational Environmental Satellite) to expose the vertical structure of precipitation during different phases of the precipitating cloud systems (PCSs). A PCS is defined using the GPM based near surface rainfall data and then GOES-based brightness temperature (BT) is used to identify a specific phase of PCS. In particular 9 hours of BT (e.g., time series of BT) data for a GPM DPR overpass is used to a specific phase of PCS. Once, all the PCSs are identified into a specific phase, their statistical properties are studied. The highest convective fraction area (~26%) and near surface rain rate (RR; 4.97 mm hr−1) are observed in developing phase of PCSs. Also, the convective fraction area and near surface RR decreases as cloud matures, and, least convective fraction area and RR (~4.11 mm hr−1) are observed in dissipating phase PCSs. The vertical structure of precipitation consists of more complex relation among different phases of PCSs. The vertical distributors of hydrometeors (e.g., radar reflectivity, RR, and DSD parameters) during various phases have different characteristics above and below the freezing height (~5 km). For example, convective precipitation has small concentration of higher sized hydrometeors below the freezing height, whereas mature has PCSs show different behavior. The total amount of water analysis shows that liquid and ice water amount varies during different phases and affect the rainfall characteristics. It is observed that precipitation characteristics during different phases are influenced by the Andes Mountain and developing phase PCSs have higher sized of hydrometeors with higher near surface RR at the north-eastern continent of Peru.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

The Observed Water Vapor Budget in an Atmospheric River over the Northeast Pacific

AbstractCombined airborne, shipboard, and satellite measurements provide the first observational assessment of all major terms of the vertically integrated water vapor (IWV) budget for a 150 km × 160 km region within the core of a strong atmospheric river over the northeastern Pacific Ocean centered on 1930 UTC 5 February 2015. Column-integrated moisture flux convergence is estimated from eight dropsonde profiles, and surface rain rate is estimated from tail Doppler radar reflectivity measurements. Dynamical convergence of water vapor (2.20 ± 0.12 mm h−1) nearly balances estimated precipitation (2.47 ± 0.41 mm h−1), but surface evaporation (0.0 ± 0.05 mm h−1) is negligible. Advection of drier air into the budget region (−1.50 ± 0.21 mm h−1) causes IWV tendency from the sum of all terms to be negative (−1.66 ± 0.45 mm h−1). An independent estimate of IWV tendency obtained from the difference between IWV measured by dropsonde and retrieved by satellite 3 h earlier is less negative (−0.52 ± 0.24 mm h−1), suggesting the presence of substantial temporal variability that is smoothed out when averaging over several hours. The calculation of budget terms for various combinations of dropsonde subsets indicates the presence of substantial spatial variability at ~50-km scales for precipitation, moisture flux convergence, and IWV tendency that is smoothed out when averaging over the full budget region. Across subregions, surface rain rate is linearly proportional to dynamical convergence of water vapor. These observational results improve our understanding of the thermodynamic and kinematic processes that control IWV in atmospheric rivers and the scales at which they occur.

Raindrop Signature from Microwave Radiometer Over Deserts

AbstractRainfall estimates from spaceborne microwave radiometers form the foundation of global precipitation data sets. Since the beginning of the satellite microwave rainfall estimation era in the 1980s, the primary signature leveraged over land for these estimates has been the brightness temperature (TB) depression due to ice particle scattering. Contrary to this practice, time series analyses based on observations from two spaceborne radars and two spaceborne radiometers reveal a TB increase at H19 due to raindrop emission as the primary cloud particle signature over desert terrain. Low surface emissivity supports the use of liquid raindrop emission as the primary signature over desert surfaces. In these regions, the surface rain rate better correlates with the liquid raindrop emission signal than with the scattering induced by ice further aloft, suggesting a new potential for improving rainfall estimation over deserts by exploiting the liquid raindrop emission signature.