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Typhoon Bolaven caused significant damage with severe rainfall all over South Korea, including Cheju Island, which received more than 250 mm in 2 days in August 2012. It was regarded as the most powerful storm to strike the Korean Peninsula in nearly a decade. The rainfall-rate datasets were obtained from S-band radar operated by the Korea Meteorological Administration to be analyzed and compared with the mesoscale Cloud Resolving Storm Simulator (CReSS) model simulation. Multifractal analysis was conducted to understand the structure of the rainfall rate with height in the typhoon system. The radar rainfall data presented with strong intermittency across scales at lower altitudes (1 and 2 km) and a more homogeneous rainfall field at high altitude (5 km) with two parameters (fractal codimension and multifractality index). The statistical scaling moment function and maximal singularities show clear significant differences between radar and the CReSS model. [ABSTRACT FROM AUTHOR]

The potential of rainfall-rate assignment using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Instrument (SEVIRI) data is investigated. For this purpose, a new conceptual model for precipitation processes in connection with midlatitude cyclones is developed, based on the assumption that high rainfall rates are linked to a high optical thickness and a large effective particle radius, whereas low rainfall rates are linked to a low optical thickness and a small effective particle radius. Reflection values in the 0.56–0.71- μm (VIS0.6) and 1.5–1.78- μm (NIR1.6) channels, which provide information about the optical thickness and the effective radius, are considered in lieu of the optical and microphysical cloud properties. An analysis of the relationship between VIS0.6 and NIR1.6 reflection and the ground-based rainfall rate revealed a high correlation between the sensor signal and the rainfall rate. Based on these findings, a method for rainfall-rate assignment as a function of VIS0.6 and NIR1.6 reflection is proposed. The validation of the proposed technique showed encouraging results, especially for temporal resolutions of 6 and 12 h. This is a significant improvement compared to existing IR retrievals, which obtain comparable results for monthly resolution. The existing relationship between the VIS0.6 and NIR1.6 reflection values and the ground-based rainfall rate is corroborated with the new conceptual model. The good validation results indicate the high potential for rainfall retrieval in the midlatitudes with the high spatial and temporal resolution provided by MSG SEVIRI. [ABSTRACT FROM AUTHOR]

Rainfall-rate statistics are frequently derived on the basis of rain gauge recordings with effective integration times of 10 min or longer. The conversion of such data to equivalent statistics for an effective integration time of 1 min is very important to predict rainfall attenuation at a location accurately. Because of the limited availability of 1-min rainfall-rate data, conversion of available rainfall-rate distributions with longer integration times to the desired 1-min distributions is needed. Several methods for converting 60-min rainfall rates to 1-min rainfall rates are tested against measured rainfall rates from tropical, high-rainfall-rate regions. Segal’s method was found to perform the best overall when compared with other conversion methods. [ABSTRACT FROM AUTHOR]

In 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results. [ABSTRACT FROM AUTHOR]

Polarization radar offers the promise of much more accurate rainfall-rate R estimates than are possible from radar reflectivity factor Z alone, not only by better characterization of the drop size distribution, but also by more reliable correction for attenuation and the identification of hail. However, practical attempts to implement retrieval algorithms have been hampered by the difficulty in coping with the inherent noise in the polarization parameters. In this paper, a variational retrieval scheme is described that overcomes these problems by employing a forward model for differential reflectivity Zdr and differential phase shift φdp and iteratively refining the coefficient a in the relationship Z = aRb such that the difference between the forward model and the measurements is minimized in a least squares sense. Two methods are used to ensure that a varies smoothly in both range and azimuth. In range, a is represented by a set of cubic-spline basis functions; in azimuth, the retrieval at one ray is used as a constraint on the next. The result of this smoothing is that the retrieval is tolerant of random errors in Zdr of up to 1 dB and in φdp of up to 5°. Correction for attenuation is achieved simply and effectively by including its effects in the forward model. If hail is present then the forward model is unable to match the observations of Zdr and φdp simultaneously. This enables a first pass of the retrieval to be used to identify the radar pixels that contain hail, followed by a second pass in which the fraction of the Z in those gates that is due to hail is retrieved, this time with the scheme being able to forward-model both Zdr and φdp accurately. The scheme is tested on S-band radar data from southern England in cases of rain, spherical hail, oblate hail, and mixtures of rain and hail. It is found to be robust and stable, even in the presence of differential phase shift on backscatter. [ABSTRACT FROM AUTHOR]

The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters (Dm) on the SETP are the smallest among six sites. The generalized intercept parameter (Nw) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement: For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)'s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied. [ABSTRACT FROM AUTHOR]

This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity ZDR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and ZDR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation. [ABSTRACT FROM AUTHOR]

Precipitation characteristics of trade wind clouds over the Atlantic Ocean near Barbuda are derived from radar and aircraft data and are compared with satellite-observed cloud fields collected during the Rain in Cumulus over the Ocean (RICO) field campaign. S-band reflectivity measurements Z were converted to rainfall rates R using a Z–R relationship derived from aircraft measurements. Daily rainfall rates varied from 0 to 22 mm day-1. The area-averaged rainfall rate for the 62-day period was 2.37 mm day-1. If corrected for evaporation below cloud base, this value is reduced to 2.23 mm day-1, which translates to a latent heat flux to the atmosphere of 63 W m-2. When compared with the wintertime ocean-surface latent heat flux from this region, the average return of water to the ocean through precipitation processes within the trade wind layer during RICO was 31%–39%. A weak diurnal cycle was observed in the area-averaged rainfall rate. The magnitude of the rainfall and the frequency of its occurrence had a maximum in the predawn hours and a minimum in the midmorning to early afternoon on 64% of the days. Radar data were collocated with data from the Multiangle Imaging Spectroradiometer (MISR) to develop relationships between cloud-top height, cloud fraction, 866-nm bidirectional reflectance factor (BRF), and radar-derived precipitation. The collocation took place at the overpass time of ∼1045 local time. These relationships revealed that between 5.5% and 10.5% of the cloudy area had rainfall rates that were > 0.1 mm h-1, and between 1.5% and 3.5% of the cloudy area had rainfall rates that were >1 mm h-1. Cloud-top heights between ∼3 and 4 km and BRFs between 0.4 and 1.0 contributed ∼50% of the total rainfall. For cloudy pixels having detectable rain, average rainfall rates increased from ∼1 to 4 mm h-1 as cloud-top heights increased from ∼1 to 4 km. Rainfall rates were closely tied to the type of mesoscale organization, with much of the rainfall originating from shallow (<5 km) cumulus clusters shaped as arcs associated with cold-pool outflows. [ABSTRACT FROM AUTHOR]

This paper discusses the basis for a new rainfall estimation method using geostationary infrared and visible data. The precipitation radar on board the Tropical Rainfall Measuring Mission satellite is used to train the algorithm presented (which is the basis of the estimation method) and the further intercomparison. The algorithm uses daily Geostationary Operational Environmental Satellite infrared–visible (IR–VIS) cloud classifications together with radiative and evolution properties of clouds over the life cycle of mesoscale convective systems (MCSs) in different brightness temperature (Tb) ranges. Despite recognition of the importance of the relationship between the life cycle of MCSs and the rainfall rate they produce, this relationship has not previously been quantified precisely. An empirical relationship is found between the characteristics that describe the MCSs’ life cycle and the magnitude of rainfall rate they produce. Numerous earlier studies focus on this subject using cloud-patch or pixel-based techniques; this work combines the two techniques. The algorithm performs reasonably well in the case of convective systems and also for stratiform clouds, although it tends to overestimate rainfall rates. Despite only using satellite information to initialize the algorithm, satisfactory results were obtained relative to the hydroestimator technique, which in addition to the IR information uses extra satellite data such as moisture and orographic corrections. This shows that the use of IR–VIS cloud classification and MCS properties provides a robust basis for creating a future estimation method incorporating humidity Eta field outputs for a moisture correction, digital elevation models combined with low-level moisture advection for an orographic correction, and a nighttime cloud classification. [ABSTRACT FROM AUTHOR]

This study analyzed the regional characteristics of raindrop size distribution (DSD) in the southern coastal area of South Korea. Data from March 2016 to February 2017 were recorded Uy four Particle Size Velocity (PARSIVEL) disdrometers installed at intervals of ~20 km from the coastline to inland areas. Within 20 km from the coastline, multiple local maxima in the probability density function (PDF) were observed at mass-weighted mean diameter Dm = 0.6 mm and normalized intercept parameter logNw = 5.2 for stratiform rainfall, but these features were not observed more than 20 km from the coastline. On the basis of mean Dm-logNw values, stratiform rainfall clearly differed between coastal and inland areas. For convective rainfall, there was a linear relationship between Dm and Nw with distance from the coastline. PDF analyses of diurnal variation in DSD confirmed that in spring and autumn multiple local maxima appear in the daytime. The multiple local maxima in Dm and logNw were respectively lower and higher at nighttime than in the daytime in the spring and summer season. These features were highly dependent on the prevailing wind. There was a pattern of increasing A and decreasing b in the radar reflectivity-rainfall rate (Z-R) relationship (Z = ARb) with distance from the coastline, and these features were more pronounced in convective rainfall. These diurnal variabilities were regular in stratiform rainfall, and there were large differences in quantitative precipitation estimation depending on the land or sea breeze in the coastal area. [ABSTRACT FROM AUTHOR]

The relatively low density of weather radar networks can lead to low-altitude coverage gaps. As existing networks are evaluated for gap fillers and new networks are designed, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling quantitative precipitation estimation (QPE) differences based on network density, antenna aperture, and polarimetric bias. Thousands of cases from the warm-season months of May–August 2015–17 are processed using both the specific attenuation [R(A)] and reflectivity–differential reflectivity [R(Z, ZDR)] QPE methods and are compared with Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified on the basis of beam height, cross-radial resolution, added polarimetric bias, and observed rainfall rate. The collected data are used to construct a support vector machine regression model that is applied to the current WSR-88D network for holistic error quantification. An analysis of the effects of polarimetric bias on flash-flood rainfall rates is presented. Rainfall rates that are based on 2-yr/1-h return rates are used for a contiguous-U.S.-wide analysis of QPE errors in extreme rainfall situations. These errors are then requantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Last, a gap-filling scenario utilizing the QPE error model, flash-flood rainfall rates, population density, and potential additional WSR-88D sites is presented, exposing the highest-benefit coverage holes in augmenting the WSR-88D network (or a future network) relative to QPE performance. [ABSTRACT FROM AUTHOR]

Extended, high-resolution measurements of vertical air motion and median volume drop diameter D0 in widespread precipitation from three diverse Atmospheric Radiation Measurement Program (ARM) locations [Lamont, Oklahoma, Southern Great Plains site (SGP); Niamey, Niger; and Black Forest, Germany] are presented. The analysis indicates a weak (0-10 cm−1) downward air motion beneath the melting layer for all three regions, a magnitude that is to within the typical uncertainty of the retrieval methods. On average, the hourly estimated standard deviation of the vertical air motion is 0.25 m s−1 with no pronounced vertical structure. Profiles of D0 vary according to region and rainfall rate. The standard deviation of 1-min-averaged D0 profiles for isolated rainfall rate intervals is 0.3-0.4 mm. Additional insights into the form of the raindrop size distribution are provided using available dual-frequency Doppler velocity observations at SGP. The analysis suggests that gamma functions better explain paired velocity observations and radar retrievals for the Oklahoma dataset. This study will be useful in assessing uncertainties introduced in the measurement of precipitation parameters from ground-based and spaceborne remote sensors that are due to small-scale variability. [ABSTRACT FROM AUTHOR]

The gamma drop size distribution (DSD) has been widely used in the meteorological community for years to model observed DSD. It has been found that the relation between the slope (Λ) and shape (μ) parameters of the gamma DSD can be empirically described by a polynomial of second degree. In this article, on the basis of disdrometer-measured DSDs from seven independent precipitation events associated with different weather systems, an empirical μ–Λ relation that is slightly different from those reported by other scientists is obtained by best fitting a quadratic polynomial to observed data. In addition to the empirical relation, a μ–Λ relation is derived based on theoretical relations between gamma DSD moments and Λ and μ. It is shown that the derived μ–Λ relation is independent of the order of the moment of the gamma DSD. The key factor dominating the μ–Λ relation is the ratio of the number density parameter N(Dm) to total number density of the raindrop M0, where Dm is the mean diameter of the DSD. It is further shown that the skewness and the variance of the DSD determine the magnitude of the ratio N(Dm)/M0 that governs the slope of the μ–Λ relation. A comparison between the derived and the empirical μ–Λ relations shows that their behaviors are very similar, especially for large rainfall rates characterized by smaller Λ and μ values. Moreover, the ratio N(Dm)/M0 bears a weak relation to the rainfall rate R. Nevertheless, the square of the ratio M0/N(Dm) is closely related to the ratio R/M0 and their relation can also be described by a second-degree polynomial. Considering this property, the authors examine the validity of the various μ–Λ relations by simulating the relations between R/M0 and [M0/N(Dm)]2. A comparison between observed and simulated results shows that the relation generated from the derived μ–Λ relation bears the best resemblance to the observed one in both magnitude and shape. [ABSTRACT FROM AUTHOR]

Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial-duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e., the 2-yr storm) to 1% (i.e., the 100-yr storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the mid-Atlantic region. At approximately one-quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations, the rainfall extremes based on the 1950–99 partial-duration series show a significant (p > 0.90) change in the 100-yr ARI relative to the 1950–2019 period. Collectively, the results indicate that, given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-yr intervals. Significance Statement: Engineering design standards for drainage systems, dams, and other infrastructure rely on analyses of precipitation extremes. Often such structures are designed on the basis of the probability of exceeding a specified rainfall rate in a given year. The frequency of extreme rainfall events has increased in the mid-Atlantic region of the United States in recent decades, leading us to evaluate how these changes have affected these exceedance probabilities. From 2000 through 2019, there has been a consistent increase of generally 2.5%–5.0% in design rainfall amounts. The increase is similar across a range of rainfall durations from 1 h to 20 days and also annual exceedance probabilities ranging from 50% to 1% (i.e., from the "2-yr storm" to the "100-yr storm"). The work highlights the need to routinely update the climatological extreme-value analyses used in engineering design, with the results suggesting that a 20-yr cycle might be an appropriate update frequency. [ABSTRACT FROM AUTHOR]

Knutson et al. recently published a metastudy that gives multimodel projections for changes in various properties of tropical cyclones under climate change. They considered frequency of tropical cyclones, frequency of very intense tropical cyclones, intensity of tropical cyclones, and total rainfall rate of tropical cyclones. For each of these properties, they reported changes globally and by basin for the six major tropical cyclone basins. The changes were pre- sented as the change that would occur with 28C warming of global mean surface temperature. These projections are po- tentially of great use to the tropical cyclone risk modeling community. However, most risk models use temporal baselines, such as the period from 1950 to 2019, and the Knutson et al. results can only be applied to risk models after some steps of adjustment involving past and future global mean surface temperature values. We derive the necessary adjustments and present and discuss some of the resulting projections, for different properties, basins, RCPs, and baselines.We find that the results are sensitive to the baseline being used, which implies that users of tropical cyclone risk models need to make sure they clearly understand what baseline their model represents before they adjust the model for climate change. One part of our analysis derives estimates of the implied impact of climate change so far on TC properties, relative to a representative baseline. The computer code we use to calculate the adjustments is available online. [ABSTRACT FROM AUTHOR]

A new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm. The technique is realized in three steps: (i) precipitating cloud areas are identified, (ii) the areas are separated into convective and advective-stratiform precipitating areas, and (iii) rainfall rates are assigned separately to the convective and advective-stratiform precipitating areas. Validation studies were carried out for each individual step as well as for the overall procedure using collocated ground-based radar data. Regarding each individual step, the models for rain area and convective precipitation detection produce good results. Both retrieval steps show a general tendency toward elevated prediction skill during summer months and daytime. The RF models for rainfall-rate assignment exhibit similar performance patterns, yet it is noteworthy how well the model is able to predict rainfall rates during nighttime and twilight. The performance of the overall procedure shows a very promising potential to estimate rainfall rates at high temporal and spatial resolutions in an automated manner. The near-real-time continuous applicability of the technique with acceptable prediction performances at 3-8-hourly intervals is particularly remarkable. This provides a very promising basis for future investigations into precipitation estimation based on machine-learning approaches and MSG SEVIRI data. [ABSTRACT FROM AUTHOR]

This study reports on the presence of suspicious 'extreme rainfall' data in the 2A25 version-7 (V7) product of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) dataset and introduces a simple method for detecting and filtering out the suspicious data. These suspicious data in V7 are found by comparing the extreme rainfall characteristics in the V7 and version-6 products. Most of the suspicious extremes are located over land, especially in mountainous regions. Radar reflectivities in the suspicious extremes show significant monotonic increases toward the echo bottom. These facts indicate that the suspicious extremes are mainly caused by contamination from ground or sea clutter. A simple thresholding filter for eliminating the suspicious extreme data is developed using common characteristics in the horizontal and vertical rainfall structures and reflectivities in the suspicious extremes. The proposed filter mitigates deformations in the frequency distribution of the surface rainfall rate in the 2A25 V7 product. [ABSTRACT FROM AUTHOR]

A response by Yen-Hsyang Chu, Chaou-Hain Chen and Ching-Lun Su to a letter to the editor concerning empirical second-degree polynomial.

The spatial variability of raindrop size distributions (DSDs) and precipitation fields is investigated utilizing disdrometric measurements from the four Precipitation Occurrence Sensor Systems (POSS) and radar reflectivity fields from S-band dual-polarization radar and vertically pointing X-band radar. The spatial cross correlation of the moments of DSDs, their ratio, error in rainfall estimate, and normalization parameters are quantified using a “noncentered” correlation function. The time-averaged spatial autocorrelation function of observed radar reflectivity factor (Ze) is smaller than that of estimated rainfall rate from Ze because of power-law R–Z transformation with its exponent larger than unity. The important spatial variability of DSDs and rain integral fields is revealed by the significant differences among average DSDs and leads to an average fractional error of 25% in estimating rainfall accumulation during an event. The spatial correlation of the reflectivity from POSS is larger than that of Ze because of larger measurement noise in Ze. The higher moments of DSDs are less correlated in space than lower moments. The correlation of rainfall estimate error is higher than that of estimated rainfall rate and of rainfall rate calculated from DSDs. The correlation of the characteristic number density is low (0.87 at 1.3-km distance), suggesting that the assumed homogeneity of the characteristic number density in space could result in larger errors in the retrieval of DSDs and rain-related parameters. However, the characteristic diameter is highly correlated in space. [ABSTRACT FROM AUTHOR]

The spatial variability and temporal variability of precipitation are widely recognized. In particular, rainfall rates can fluctuate widely in regions where the raindrops are clustered and where mean conditions are changing (statistical heterogeneity). Indeed, at times, the ambiguity associated with an estimated average rainfall rate may become very large. Therefore, in quantitative measurements of precipitation, it would be useful to identify where this occurs. In this work a technique is proposed and applied to quantify the variability in rainfall rates introduced by statistical heterogeneity and raindrop clustering using deviations from Rayleigh statistics of intensity fluctuations. This technique separates the Rayleigh contributions to the observed relative dispersion from those arising from clustering and statistical heterogeneities. Applications to conventional meteorological radar measurements are illustrated using two scans. Often, but not always, the greatest ambiguities in estimates of the average rainfall rate occur just where the rainfall rates are the largest and presumably where accurate estimates are most important. This ambiguity is not statistical; rather, it indicates the presence of important sub-beam-scale fluctuations. As a consequence, no single average value can be applied uniformly to the entire domain. The examples provided here also demonstrate that the appropriate observations are feasible using current conventional meteorological radars with adequate processing capabilities. However, changes in radar technology that improve and increase pulse-to-pulse statistical independence will permit such observations to be gathered more routinely at finer spatial resolution and with enhanced precision. [ABSTRACT FROM AUTHOR]

The Goddard profiling algorithm (GPROF) uses Bayesian probability theory to retrieve rainfall over the global oceans. A critical component of GPROF and most Bayes theorem-based retrieval frameworks is the specification of uncertainty in the observations being utilized to retrieve the parameter of interest. In the case of GPROF, for any sensor, uncertainties in microwave brightness temperatures (Tbs) arise from radiative transfer model errors, satellite sensor noise and/or degradation, and nonlinear, scene-dependent Tb offsets added during sensor intercalibration procedures. All mentioned sources impact sensors in a varying fashion, in part because of sensor-dependent fields of view. It is found that small errors in assumed Tb uncertainty (ranging from 0.57 K at 10 GHz to 2.29 K at 85 GHz) lead to a 3.6% change in the retrieved global-average oceanic rainfall rate, and 10%-20% (20%-40%) shifts in the pixel-level (monthly) frequency distributions for given rainfall bins. A mathematical expression describing the sensitivity of retrieved rainfall to uncertainty is developed here. The strong global sensitivity is linked to rainfall variance scaling systematically as Tb varies. For ocean scenes, the same emission-dominated rainfall-Tb physics used in passive microwave rainfall retrieval is also responsible for the substantial underestimation (overestimation) of global rainfall if uncertainty is overestimated (underestimated). Proper uncertainties are required to quantify variability in surface rainfall, assess long-term trends, and provide robust rainfall benchmarks for general circulation model evaluations. The implications for assessing global and regional biases in active versus passive microwave rainfall products, and for achieving rainfall product agreement among a constellation of orbiting microwave radiometers [employed in the Global Precipitation Measurement (GPM) mission], are also discussed. [ABSTRACT FROM AUTHOR]

TRMM PR 2A25, version 7 (V7), retrievals of reflectivity Z and rainfall rate R are compared with WSR-88D dual-polarimetric S-band radar data for 28 radars over the southeastern United States after matching their horizontal resolution and sampling. TRMM Ku-band measurements are converted to S-band approximations to more directly compare reflectivity estimates. Rain rates are approximated from WSR-88D data using the CSU-hydrometeor identification rainfall optimization (HIDRO) algorithm. Tropics-wide TRMM retrievals confirm previous findings of a low overlap fraction between extreme convective intensity, as approximated by the maximum 40-dB Z height, and extreme near-surface rain rates. WSR-88D data also confirm this low overlap but show that it is likely higher than TRMM PR retrievals indicate. For maximum 40-dB Z echo heights that extend above the freezing level, mean WSR-88D reflectivities at low levels are approximately 2 dB higher than TRMM PR reflectivities. Higher WSR-88D-retrieved rain rates for a given low-level reflectivity combine with these higher low-level reflectivities for a given maximum 40-dB Z height to produce rain rates that are approximately double those retrieved by the TRMM PR for maximum 40-dB Z heights that extend above the freezing level. TRMM PR path-integrated attenuation, and WSR-88D specific differential phase, differential reflectivity, and hail fraction indicate that the TRMM PR 2A25 V7 algorithm is possibly misidentifying low-midlevel hail and/or graupel as greater attenuating liquid, or vice versa. This misidentification, coupled with underestimation of path-integrated attenuation caused by nonuniform beamfilling and higher rain rates produced by specific differential phase (KDP)- R than Z- R relationships, results in low-biased 2A25 V7 rain rates in intense convection. [ABSTRACT FROM AUTHOR]

Two methods for deriving relationships between the equivalent radar reflectivity factor Ze and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Ze-S relationships. In the secondmethod,measurements of Ze at the top of themelting layer (ML), fromradars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Ze-S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements. [ABSTRACT FROM AUTHOR]

The Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program F-16 spacecraft measures the Earth-emitted radiation at frequencies from 19 to 183 GHz. From its high-frequency channels at 91 and 150 GHz, cloud microphysical parameters can be observed at a spatial resolution of 15 km. In this study, a simplified two-stream radiative transfer model is applied for microwave applications as a three-parameter equation and then used to retrieve the ice cloud water path (IWP) and ice particle effective diameter De. Since SSMIS is a conically scanning instrument, the retrieved IWP is less dependent on scan position and is a useful product for imaging atmospheric ice-phase clouds related to precipitation. Thus, IWP is also used to estimate surface rainfall rate through the same relationship derived previously and used in Advanced Microwave Sounding Unit (AMSU-B) and Microwave Humidity Sounder applications. The SSMIS-derived ice cloud products are compared with those from other microwave instruments on the MetOp-A satellite, and both agree well in their spatial distributions. [ABSTRACT FROM AUTHOR]

A method to retrieve total vertical amounts of cloud liquid and ice in stratiform precipitating systems is described. The retrievals use measurements from the vertically pointing Ka- and W-band cloud radars operated by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and auxiliary measurements from a scanning National Weather Service radar and a ground-based disdrometer. Separation between the cloud liquid and rain is based on estimations of the total attenuation of millimeter-wavelength radar signals in the liquid hydrometeor layer. Disdrometer measurements are used for the retrieval constraints. Because the liquid phase hydrometeor retrievals use only differential measurements, they are immune to the absolute radar calibration uncertainties. Estimates of the ice cloud phase are performed using empirical relations between absolute radar reflectivity and ice water content. Data from the nearby scanning weather-service radar, which operates at a lower frequency, are used to correct cloud radar measurements observed above the freezing level for attenuation caused by the layers of liquid and melting hydrometeors and also by wet radomes of cloud radars. Polarimetric and vertical Doppler measurements from ARM cloud radars provide a distinct separation between regions of liquid and ice phases, and therefore the corresponding retrievals are performed in each region separately. The applicability of the suggested method is illustrated for a stratiform precipitation event observed at the ARM Southern Great Plains facility. Expected uncertainties for retrievals of cloud liquid water path are estimated at about 200–250 g m-2 for typical rainfall rates observed in stratiform systems (∼3–4 mm h-1). These uncertainties increase as rainfall rate increases. The ice water path retrieval uncertainties can be as high as a factor of 2. [ABSTRACT FROM AUTHOR]

This paper presents a new statistical algorithm to estimate rainfall over the Amazon Basin region using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm relies on empirical relationships derived for different raining-type systems between coincident measurements of surface rainfall rate and 85-GHz polarization-corrected brightness temperature as observed by the precipitation radar (PR) and TMI on board the TRMM satellite. The scheme includes rain/no-rain area delineation (screening) and system-type classification routines for rain retrieval. The algorithm is validated against independent measurements of the TRMM–PR and S-band dual-polarization Doppler radar (S-Pol) surface rainfall data for two different periods. Moreover, the performance of this rainfall estimation technique is evaluated against well-known methods, namely, the TRMM-2A12 [the Goddard profiling algorithm (GPROF)], the Goddard scattering algorithm (GSCAT), and the National Environmental Satellite, Data, and Information Service (NESDIS) algorithms. The proposed algorithm shows a normalized bias of approximately 23% for both PR and S-Pol ground truth datasets and a mean error of 0.244 mm h-1 (PR) and -0.157 mm h-1 (S-Pol). For rain volume estimates using PR as reference, a correlation coefficient of 0.939 and a normalized bias of 0.039 were found. With respect to rainfall distributions and rain area comparisons, the results showed that the formulation proposed is efficient and compatible with the physics and dynamics of the observed systems over the area of interest. The performance of the other algorithms showed that GSCAT presented low normalized bias for rain areas and rain volume [0.346 (PR) and 0.361 (S-Pol)], and GPROF showed rainfall distribution similar to that of the PR and S-Pol but with a bimodal distribution. Last, the five algorithms were evaluated during the TRMM–Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA) 1999 field campaign to verify the precipitation characteristics observed during the easterly and westerly Amazon wind flow regimes. The proposed algorithm presented a cumulative rainfall distribution similar to the observations during the easterly regime, but it underestimated for the westerly period for rainfall rates above 5 mm h-1. NESDIS1 overestimated for both wind regimes but presented the best westerly representation. NESDIS2, GSCAT, and GPROF underestimated in both regimes, but GPROF was closer to the observations during the easterly flow. [ABSTRACT FROM AUTHOR]

Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the Intermountain West of the United States. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) Model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multiscale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder–feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding. Significance Statement: Understanding snowfall amounts and distribution in the mountains and how it is linked to topography, synoptic flow, and microphysical processes will help in the development of effective strategies for cloud seeding operations, managing runoff, reservoir, and mitigating flood risks, garnering substantial interest from stakeholders and the government agencies. [ABSTRACT FROM AUTHOR]

Situated in the Upper Midwest, Minnesota's midcontinental location places it in a climate transition zone between eastern U.S. humid conditions and western semiarid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May–September 1959–2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural network, self-organizing map (SOM) technique to identify sea level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed that favorable conditions related to moisture and lift were associated with heavy rainfall. [ABSTRACT FROM AUTHOR]

The accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the ' R( ZHH, ZDR)' power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R( ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD). [ABSTRACT FROM AUTHOR]

In aviation meteorology, METAR messages are used to disseminate the existence of cumulonimbus (Cb) clouds. METAR messages are traditionally constructed manually from human observations, but there is a growing trend toward automation of this process. At the Finnish Meteorological Institute (FMI), METAR messages incorporate an operational automatic detection of Cb based solely on weather radar data, when manual observations are not available. However, the verification of this automatic Cb detection is challenging, as good ground truth data are not often available; even human observations are not perfect as Cb clouds can be obscured by other clouds, for example. Therefore, statistical estimation of the relevant verification measures from imperfect observations using latent class analysis (LCA) was explored. In addition to radar-based products and human observations, the convective rainfall rate from EUMETSAT's Nowcasting Satellite Application Facility and lightning products from the Finnish lightning network were used for determining the existence of Cb clouds. Results suggest that LCA gives reasonable estimates of verification measures and, based on these estimates, the Cb detection system at FMI gives results comparable to human observations. [ABSTRACT FROM AUTHOR]

A dataset containing 9637 h of two-dimensional video disdrometer observations consisting of more than 240 million raindrops measured at diverse climatological locations was compiled to help characterize underlying drop size distribution (DSD) assumptions that are essential to make precise retrievals of rainfall using remote sensing platforms. This study concentrates on the tail of the DSD, which largely impacts rainfall retrieval algorithms that utilize radar reflectivity. The maximum raindrop diameter was a median factor of 1.8 larger than the mass-weighted mean diameter and increased with rainfall rate. Only 0.4% of the 1-min DSD spectra were found to contain large raindrops exceeding 5 mm in diameter. Large raindrops were most abundant at the tropical locations, especially in Puerto Rico, and were largely concentrated during the spring, especially at subtropical locations. Giant raindrops exceeding 8 mm in diameter occurred at tropical, subtropical, and high-latitude continental locations. The greatest numbers of giant raindrops were found in the subtropical locations, with the largest being a 9.7-mm raindrop that occurred in northern Oklahoma during the passage of a hail-producing thunderstorm. These results suggest large raindrops are more likely to fall from clouds that contain hail, especially those raindrops exceeding 8 mm in diameter. [ABSTRACT FROM AUTHOR]

Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN's accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning. [ABSTRACT FROM AUTHOR]

In this paper, statistical properties of rainfall are derived from 14 years of Tropical Rainfall Measuring Mission data to optimize the use of flight hours for the upcoming High Altitude Ice Crystals (HAIC)/High Ice Water Content (HIWC) program. This program aims to investigate the convective processes responsible for the generation of the high ice water content that has been recognized as a threat to civil aviation. The probability that convective cells are conducive to HIWC is also further investigated using three years of C-band polarimetric radar data. Further insights into the variability of convective rainfall and favorable conditions for HIWC are also gained using two different methods to characterize the large-scale atmospheric conditions around Darwin, Australia (the Madden-Julian oscillation and the Darwin atmospheric regimes), and the underlying surface type (oceanic vs continental). The main results from the climatology relevant to flight-plan decision making are (i) convective cells conducive to HIWC should be found close to Darwin, (ii) at least 90% of convective cells are conducive to HIWC at 10- and 12-km flight levels, (iii) multiple flights per day in favorable large-scale conditions will be needed so as to utilize the 150 project flight hours, (iv) the largest numbers of HIWC radar pixels are found around 0300 and 1500 local time, and (v) to fulfill the requirement to fly 90 h in oceanic convection and 60 h in or around continental convection, a minimum 'acceptable' size of the convective area has been derived and should serve as a guideline for flight-decision purposes. [ABSTRACT FROM AUTHOR]

A spatiotemporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of limited and infrequent measurements while accounting for the measurement accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. The technique is developed using synthetic radar data. Nine months of Operational Program for the Exchange of Weather Radar (OPERA) data are used on grid sizes of 100, 248, and 500 km with pixel resolutions of 8, 12, and 24 km to simulate satellite fields of view and are then applied to the real satellite data over the Southwest to calculate 3-h rainfall accumulations. The results are compared with the simple averaging technique, which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. Using synthetic data, depending on the time separation of the measurements and their accuracy, a spatiotemporal correlation technique has shown the potential to yield improvements of up to 40% in absolute error and up to 25% in root-mean-square error when compared with the simple averaging technique. When applied to the real satellite data over the Southeast, the technique showed much less skill (general improvement of only 2%-6%). [ABSTRACT FROM AUTHOR]

Although zonal mean rain rates from the Tropical Rainfall Measuring Mission (TRMM) are in good (<10%%) agreement between the TRMM Microwave Imager (TMI) and precipitation radar (PR) rainfall algorithms, significant uncertainties remain in some regions where these estimates differ by as much as 30%% over the period of record. Previous comparisons of these algorithms with ground validation (GV) rainfall have shown significant (>10%%) biases of differing sign at various GV locations. Reducing these biases is important in the context of developing a database of cloud profiles for passive microwave retrievals that is based upon the PR-measured profiles. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution, and cloud water path (cLWP) are retrieved for each radar profile. The modular nature of the framework provides the opportunity to test the sensitivity of the retrieval to the inclusion of different measurements, retrieved parameters, and models for microwave scattering properties of hydrometeors. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, Florida, shows agreement within 2%%, which exceeds previous algorithms'' ability to match rainfall at these two sites. Errors between observed and simulated brightness temperatures are reduced and climatological features of the DSD, as measured by disdrometers at these two locations, are also reproduced in the output of the combined algorithm. [ABSTRACT FROM AUTHOR]

The differential phase (ΦDP) measured by polarimetric radars is recognized to be a very good indicator of the path integrated by rain. Moreover, if a linear relationship is assumed between the specific differential phase (KDP) and the specific attenuation (AH) and specific differential attenuation (ADP), then attenuation can easily be corrected. The coefficients of proportionality, γH and γDP, are, however, known to be dependent in rain upon drop temperature, drop shapes, drop size distribution, and the presence of large drops causing Mie scattering. In this paper, the authors extensively apply a physically based method, often referred to as the “Smyth and Illingworth constraint,” which uses the constraint that the value of the differential reflectivity ZDR on the far side of the storm should be low to retrieve the γDP coefficient. More than 30 convective episodes observed by the French operational C-band polarimetric Trappes radar during two summers (2005 and 2006) are used to document the variability of γDP with respect to the intrinsic three-dimensional characteristics of the attenuating cells. The Smyth and Illingworth constraint could be applied to only 20% of all attenuated rays of the 2-yr dataset so it cannot be considered the unique solution for attenuation correction in an operational setting but is useful for characterizing the properties of the strongly attenuating cells. The range of variation of γDP is shown to be extremely large, with minimal, maximal, and mean values being, respectively, equal to 0.01, 0.11, and 0.025 dB °-1. Coefficient γDP appears to be almost linearly correlated with the horizontal reflectivity (ZH), differential reflectivity (ZDR), and specific differential phase (KDP) and correlation coefficient (ρHV) of the attenuating cells. The temperature effect is negligible with respect to that of the microphysical properties of the attenuating cells. Unusually large values of γDP, above 0.06 dB °-1, often referred to as “hot spots,” are reported for 15%—a nonnegligible figure—of the rays presenting a significant total differential phase shift (ΔφDP > 30°). The corresponding strongly attenuating cells are shown to have extremely high ZDR (above 4 dB) and ZH (above 55 dBZ), very low ρHV (below 0.94), and high KDP (above 4° km-1). Analysis of 4 yr of observed raindrop spectra does not reproduce such low values of ρHV, suggesting that (wet) ice is likely to be present in the precipitation medium and responsible for the attenuation and high phase shifts. Furthermore, if melting ice is responsible for the high phase shifts, this suggests that KDP may not be uniquely related to rainfall rate but can result from the presence of wet ice. This hypothesis is supported by the analysis of the vertical profiles of horizontal reflectivity and the values of conventional probability of hail indexes. [ABSTRACT FROM AUTHOR]

A procedure for the estimation of rainfall rate, capitalizing on a radar-based raindrop size distribution (RSD) parameter retrieval and neural network (NN) inversion techniques, is validated using an extensive and quality-controlled archive. The RSD retrieval algorithm utilizes polarimetric variables measured by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), through an ad hoc regularized neural network method. Evaluation of rainfall estimation from the NN-based method is accomplished using a large radar data and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign. Point estimates of hourly rainfall accumulations and instantaneous rainfall rates from NN-based and parametric polarimetric rainfall relations are compared with dense surface gauge observations. Rainfall accumulations from RSD retrieval-based methods are shown to be sensitive to the choice of a raindrop fall speed model. To minimize the impact of this choice, a new “direct” neural network approach is tested. Proposed NN-based approaches exhibit bias and root-mean-square error characteristics comparable with those obtained from parametric relations, specifically optimized for the JPOLE dataset, indicating an appealing generalization capability with respect to the climatological context. All tested polarimetric relations are shown to be sensitive to hail contamination as inferred from the results of automatic polarimetric echo classification and available storm reports. [ABSTRACT FROM AUTHOR]

Lightning data from the Pacific Lightning Detection Network (PacNet) and Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite were compared with TRMM precipitation radar products and latent heating and hydrometeor data. Three years of data over the central North Pacific Ocean were analyzed. The data were divided into winter (October–April) and summer (June–September) seasons. During the winter, the thunderstorms were typically embedded in cold fronts associated with eastward-propagating extratropical cyclones. Summer thunderstorms were triggered by cold upper-level lows associated with the tropical upper-tropospheric trough (TUTT). Concurrent lightning and satellite data associated with the storms were averaged over 0.5° × 0.5° grid cells and a detection efficiency correction model was applied to quantify the lightning rates. The results of the data analysis show a consistent logarithmic increase in convective rainfall rate with increasing lightning rates. Moreover, other storm characteristics such as radar reflectivity, storm height, ice water path, and latent heat show a similar logarithmic increase. Specifically, the reflectivity in the mixed-phase region increased significantly with lightning rate and the lapse rate of Z decreased; both of these features are well-known indicators of the robustness of the cloud electrification process. In addition, the height of the echo tops showed a strong logarithmic correlation with lightning rate. These results have application over data-sparse ocean regions by allowing lightning-rate data to be used as a proxy for related storm properties, which can be assimilated into NWP models. [ABSTRACT FROM AUTHOR]

A procedure to accurately resample spaceborne and ground-based radar data is described and then is applied to the measurements taken from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the ground-based Weather Surveillance Radar-1988 Doppler (WSR-88D or WSR) for the validation of the PR measurements and estimates. Through comparisons with the well-calibrated, nonattenuated WSR at Melbourne, Florida, for the period 1998–2007, the calibration of the PR aboard the TRMM satellite is checked using measurements near the storm top. Analysis of the results indicates that the PR, after taking into account differences in radar reflectivity factors between the PR and WSR, has a small positive bias of 0.8 dB relative to the WSR, implying a soundness of the PR calibration in view of the uncertainties involved in the comparisons. Comparisons between the PR and WSR reflectivities are also made near the surface for evaluation of the attenuation-correction procedures used in the PR algorithms. It is found that the PR attenuation is accurately corrected in stratiform rain but is underestimated in convective rain, particularly in heavy rain. Tests of the PR estimates of rainfall rate are conducted through comparisons in the overlap area between the TRMM overpass and WSR scan. Analyses of the data are made both on a conditional basis, in which the instantaneous rain rates are compared only at those pixels at which both the PR and WSR detect rain, and an unconditional basis, in which the area-averaged rain rates are estimated independently for the PR and WSR. Results of the conditional rain comparisons show that the PR-derived rain is about 9% greater and 19% less than the WSR estimates for stratiform and convective storms, respectively. Overall, the PR tends to underestimate the conditional mean rain rate by 8% for all rain categories, a finding that conforms to the results of the area-averaged rain (unconditional) comparisons. [ABSTRACT FROM AUTHOR]

A generalized method is presented to derive a “two scale” raindrop size distribution (DSD) model over a spatial or temporal domain in which a statistical rain parameter relation exists. The two-scale model is generally defined as a model in which one DSD parameter is allowed to vary rapidly and the other is constant over a certain space or time domain. The existence of a rain parameter relation such as the radar reflectivity–rainfall rate (Z–R) relation over a spatial or temporal domain is an example of such a two-scale DSD model. A procedure is described that employs a statistical rain parameter relation with an assumption of the gamma DSD model. An example using Z–R relations obtained at Kototabang, West Sumatra, is presented. The result shows that the resulting two-scale DSD model expressed by conventional DSD parameters depends on the assumed value of parameter μ while rain parameter relations such as k–Ze relations from those models using different μ values are very close to each other, indicating the stability of the model against the variation of μ and the validity of this method. The result is applied to the DSD model for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar 2A25 (versions 5 and 6) algorithm. The derivation procedure of the 2A25 DSD model is described. Through the application of this model, it has become possible to make a logically well-organized rain profiling algorithm and reasonable rain attenuation correction and rainfall estimates, as described in an earlier paper by Iguchi et al. [ABSTRACT FROM AUTHOR]

There have been debates and differences of opinion over the validity of using drop size distribution (DSD) models to characterize precipitation microphysics and to retrieve DSD parameters from multiparameter radar measurements. In this paper, simulated and observed rain DSDs are used to evaluate moment estimators. Seven estimators for gamma DSD parameters are evaluated in terms of the biases and fractional errors of five integral parameters: radar reflectivity (ZH), differential reflectivity (ZDR), rainfall rate (R), mean volume diameter (Dm), and total number concentration (NT). It is shown that middle-moment estimators such as M234 (using the second-third-fourth moments) produce smaller errors than lower- and higher-moment estimators if the DSD follows the gamma distribution. However, if there are model errors, the performance of M234 degrades. Even though the DSD parameters can be biased in moment estimators, integral parameters are usually not. Maximum likelihood (ML) and L-moment (LM) estimators perform similarly to low-moment estimators such as M012. They are sensitive to both model error and the measurement errors of the low ends of DSDs. The overall differences among M234, M246, and M346 are not substantial for the five evaluated parameters. This study also shows that the discrepancy between the radar and disdrometer observations cannot be reduced by using these estimators. In addition, the previously found constrained-gamma model is shown not to be exclusively determined by error effects. Rather, it is equivalent to the mean function of normalized DSDs derived through Testud's approach, and linked to precipitation microphysics. [ABSTRACT FROM AUTHOR]

Raindrop size distributions (DSDs) for tropical convective storms are used to examine the relationships between the parameters of a gamma DSD, with special emphasis on their variation with the stage of the storm. Such a distinction has rarely been made before. Several storms from a variety of tropical locations are divided into storm stages according to the temporal dependence of their reflectivity factor Z, rainfall rate R, and median volume diameter D0. In most cases it is found that the DSD parameter D0 is approximately constant in time during the convective, or C, stage, which leads to a Z–R relation of the form Z = AR, that is, a linear relationship between Z and R. This finding implies the existence of equilibrium DSDs during the C stage. The convective stage is sometimes marked by pulsations in draft strength so that D0, R, and Z and associated values of the shape parameter μ decrease in a quasi-transition stage before increasing once more. Theoretical relations between the differential reflectivity ZDR and the ratio Z/R as functions of the DSD parameter μ are derived by assuming a gamma DSD and an accurate raindrop fall speed law. It is found that data derived from disdrometer observations lie along a μ = 5 isopleth for tropical continental C stages (Puerto Rico and Brazil) and along a μ = 12 isopleth for tropical maritime C stages [Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE)]. Small values of μ that occur in the weak updraft intervals do not impact the rainfall measurements because they correspond to relatively small R. The latter features imply that the measurement of rainfall for the convective stages can be performed with standard polarimetry involving only two measurables rather than three, provided knowledge of μ is available a priori. A new rain parameter diagram is presented in which isopleths of the generalized number concentration and D0 are superimposed on the Z–R plot. It is proposed that it is possible to estimate D0 from climatological and observable storm structural features, which, with Z, provide estimates of R. Such an approach is necessary for use with conventional radars until polarimetric radars are more widely available. [ABSTRACT FROM AUTHOR]

Polarimetric Doppler radars provide valuable information about the kinematic and microphysical structure of storms. However, in-depth analysis using radar products, such as Doppler-derived wind vectors and hydrometeor identification, has been difficult to achieve in (near) real time, mainly because of the large volumes of data generated by these radars, lack of quick access to these data, and the challenge of applying quality-control measures in real time. This study focuses on modifying and automating several radar-analysis and quality-control algorithms currently used in postprocessing and merging the resulting data from several radars into an integrated analysis and display in (near) real time. Although the method was developed for a specific network of four Doppler radars: two Weather Surveillance Radar-1988 Doppler (WSR-88D) radars (KFTG and KCYS) and two Colorado State University (CSU) research radars [Pawnee and CSU–University of Chicago–Illinois State Water Survey (CSU–CHILL)], the software is easily adaptable to any radar platform or network of radars. The software includes code to synthesize radial velocities to obtain three-dimensional wind vectors and includes algorithms for automatic quality control of the raw polarimetric data, hydrometeor identification, and rainfall rate. The software was successfully tested during the summers of 2004 and 2005 at the CSU–CHILL radar facility, ingesting data from the four-radar network. The display software allows users the ability to view mosaics of reflectivity, wind vectors, and rain rates, to zoom in and out of radar features easily, to create vertical cross sections, to contour data, and to archive data in real time. Despite the lag time of approximately 10 min, the software proved invaluable for diagnosing areas of intense rainfall, hail, strong updrafts, and other features such as mesocyclones and convergence lines. A case study is presented to demonstrate the utility of the software. [ABSTRACT FROM AUTHOR]

The relative skill of two vertical-profile-of-reflectivity (VPR) correction techniques for daily accumulations on a selected dataset and a real-time dataset has been verified. The first technique (C1) adjusts the 1-h rainfall amounts already derived on a Cartesian CAPPI map at an altitude of 1.5 km in a “one step” procedure using the range-dependent space–time-averaged VPR over the 1-h interval. The C2 technique corrects the nonconvective polar reflectivity measurements of each 5-min radar cycle that are also centered at a height of 1.5 km according to a VPR that is similarly derived but over a shorter time interval. The results emphasize the importance of applying a VPR correction scheme—in particular, in a climatic regime in which most of the liquid precipitation falls from stratiform echoes. The crucial importance of the choice of datasets is also underlined, causing differences in the final assessment that may be greater than those between the various algorithms. Both techniques perform well with selected events of low bright band and thus with the greatest potential for improvement—in particular, when the bias is removed in a post facto analysis. However, when the VPR algorithm is tested in a real-time environment consisting of less strong or higher brightband situations and faces a variety of day-to-day precipitation, the improvement is substantially lower. RMS errors are reduced only from 61% to 48% in contrast with the reduction from 117% to 43% seen with the smaller sample of selected events. This is because other sources of error—in particular, the variability in the radar reflectivity–rainfall rate (Z–R) relationship—are often of the same magnitude as the VPR errors. An example is provided that shows how the bias from an improper Z–R relationship can reduce the true skill of a real-time VPR correction scheme. [ABSTRACT FROM AUTHOR]

The information on the time variability of drop size distributions (DSDs) as seen by a disdrometer is used to illustrate the structure of uncertainty in radar estimates of precipitation. Based on this, a method to generate the space–time variability of the distributions of the size of raindrops is developed. The model generates one moment of DSDs that is conditioned on another moment of DSDs; in particular, radar reflectivity Z is used to obtain rainfall rate R. Based on the fact that two moments of the DSDs are sufficient to capture most of the DSD variability, the model can be used to calculate DSDs and other moments of interest of the DSD. A deterministic component of the precipitation field is obtained from a fixed R–Z relationship. Two different components of DSD variability are added to the deterministic precipitation field. The first represents the systematic departures from the fixed R–Z relationship that are expected from different regimes of precipitation. This is generated using a simple broken-line model. The second represents the fluctuations around the R–Z relationship for a particular regime and uses a space–time multiplicative cascade model. The temporal structure of the stochastic fluctuations is investigated using disdrometer data. Assuming Taylor hypothesis, the spatial structure of the fluctuations is obtained and a stochastic model of the spatial distribution of the DSD variability is constructed. The consistency of the model is validated using concurrent radar and disdrometer data. [ABSTRACT FROM AUTHOR]

Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5°-resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r ∼0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5°-resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5°-resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5°-resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm. [ABSTRACT FROM AUTHOR]

Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMWprecipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 58 scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM's dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product's uncertainty. It is found that at 58 spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%. [ABSTRACT FROM AUTHOR]

Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (>70%) satisfy a certain range of cloud properties: CER ≥ 20 µm and CTT, 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 mm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties. [ABSTRACT FROM AUTHOR]

An orographic/nonorographic rainfall classification scheme has been introduced for the operational algorithm of the Global Satellite Mapping of Precipitation (GSMaP) for passive microwave radiometers. However, problems of overestimations and false alarms of heavy orographic rainfall remain unresolved. This is because the current scheme selected lower constant thresholds of orographic rainfall conditions for global application and used values of orographically forced upward motion w derived from near-surface atmospheric data. This study improves the conceptual model of the warm-rain process for considering the strength of the upstream flow of the low-level troposphere. Under a weak upstream current, rain reaches the foothills of the windward mountain slope because of sufficient time for condensation and precipitation enhancement by the topography. Conversely, under a strong upstream current, precipitation enhancement occurs nearer to the mountain peak. This is because the upstream current flows so quickly that there is insufficient time for enhancement of precipitation over the foothills of the windward mountain slope. After implementing a variable threshold for w that depends on the mean horizontal low-level wind, the area of orographic enhancement of rain was detected reasonably well in cases of both strong and weak winds. To improve the accuracy of estimates of orographic rainfall, an adjustment to the rain estimation was introduced using a lower-frequency channel. The biases of the rainfall estimate for the adjusted scheme from the Tropical Rainfall Measuring Mission Precipitation Radar were improved for the cases considered here as well as for the Asian region of heavy orographic rainfall over land. [ABSTRACT FROM AUTHOR]

Despite its long history, improving upon current precipitation estimation techniques remains an active area of research. While many methods exist to assess precipitation, the use of satellites has allowed for near-global observation. However, satellites do not directly sense precipitation, resulting in retrieval uncertainties. Analysis of these uncertainties is typically conducted through validation studies, which, while necessary, are sensitive to local conditions. As such, predicting retrieval uncertainties where there is no validation data remains a challenge. In this study, we propose a method by which validation statistics can be extended to other regions. Using a neural network–style retrieval, the Geostationary Operational Environmental Satellite–16 (GOES-16) Precipitation Estimator using Convolutional Neural Networks (GPE-CNN), we show that, by exploiting the information content of both the satellite and ancillary meteorological data, one can predict large-scale retrieval behaviors over other regions without the need for that region's validation data. By developing classes using satellite information content, we demonstrate bias prediction improvement of up to 83% relative to a simple extension of mean bias. Including relative humidity information improves the overall prediction by up to 98% relative to the original mean bias. Although limited in scope, this method presents a pathway toward characterizing uncertainties on a broader scale. [ABSTRACT FROM AUTHOR]