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Agriculture is one of the main economic activities in the Peruvian Andes; rainwater alone irrigates more than 80% of the fields used for agriculture purposes. However, the cloud and rain generation mechanisms in the Andes still remain mostly unknown. In early 2014, the Instituto Geofísico del Perú (IGP) decided to intensify studies in the central Andes to better understand cloud microphysics; the Atmospheric Microphysics And Radiation Laboratory officially started operations in 2015 at IGP's Huancayo Observatory. In this work, a Ka-band cloud profiler [cloud and precipitation profiler (MIRA-35c)], a UHF wind profiler [Clear-Air and Rainfall Estimation (CLAIRE)], and a VHF wind profiler [Boundary Layer and Tropospheric Radar (BLTR)] are used to estimate rainfall rate at different conditions. The height dependence of the drop size diameter versus the terminal velocity, obtained by the radars, in the central Andes (3350m MSL) was evaluated. The estimates of rainfall rate are validated to ground measurements through a disdrometer [second-generation Particle, Size, and Velocity (PARSIVEL2)] and two rain gauges. The biases in the cumulative rainfall totals for the PARSIVEL2, MIRA-35c, and CLAIRE were 18%, 23%, and232%, respectively, and their respective absolute biases were 19%, 36%, and 63%. These results suggest that a real-time calibration of the radars, MIRA-35c and CLAIRE, is necessary for better estimation of precipitation at the ground. They also show that the correction of the raindrop terminal fall velocity, obtained by separating the vertical wind velocity (BLTR), used in the estimation the raindrop diameter is not sufficient, especially in convective conditions. [ABSTRACT FROM AUTHOR]

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-[micro]m ([VIS.sub.0.6]) and 1.5-1.78-[micro]m ([NIR.sub.1.6]) 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 [VIS.sub.0.6] and [NIR.sub.1.6] 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 [VIS.sub.0.6] and [NIR.sub.1.6] 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 [VIS.sub.0.6] and [NIR.sub.1.6] 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. DOI: 10.1175/2010JAMC2284.1

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 l-rain 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.

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 [Z.sub.dr] and differential phase shift [[phi].sub.dp] and iteratively refining the coefficient a in the relationship Z = a[R.sup.b] 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 [Z.sub.dr] of up to 1 dB and in [[phi].sub.dp] of up to 5[degrees]. 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 [Z.sub.dr] and chap 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 [Z.sub.dr] and [[phi].sub.dp] 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.

In this paper, the delta function is introduced to describe the observed rainfall-rate distribution including the mixture of discrete and continuous parts. Thus the observed rainfall-rate distribution can be expressed as a simple mathematical formula. Also the probability density function (pdf) of frontal rainfall in the region of the United Kingdom is investigated. It is found that the pdf during the period of 26 January-25 February 1990 can be fitted well by the lognormal distribution, and there is a linear correlation between the mean and the standard deviation of rainfall-rate pdf during this period. The GARP Atlantic Tropical Experiment (GATE) data for pixel sizes of 4.0 and 40.0 km are also analyzed in this paper. It is also found that both mean and standard deviation decrease with the increment of the observing pixel size. Based on the rainfall-rate distribution formula; the research results of Kedem et al. regarding modeling the rain-rate pdf as lognormal, gamma, and inverse Gaussian; the correlation between the mean and variance of rainfall-rate pdf; and the specified values of mean and variance, some conclusions on the threshold method (or area-time integral method) are presented that do not require the assumption that rain rate is homogeneous in time and space. The results show that the area-average rain rate and fractional area are nonlinearly related at low rain-rate thresholds and that there is variation of the regression slope with rainfall-rate threshold, observing pixel size, and rain type, and so on. From these results, it can be concluded that the rainfall-rate threshold, precipitation type, and the observing pixel size are three major factors for the threshold method (or area-time integral method). The three elements have to be considered if the threshold method (or area-time integral method) is applied. Also, it is known that the reasons that the threshold method (or area-time integral method) works well arise from the observed precipitation properties such as the lognormal rainfall-rate pdf, the correlation between the mean and variance, and the specified values of the mean and variance. Such observed precipitation properties originate from the mesoscale dynamics and cloud physics. At the same time, such observed precipitation properties should be constrained by mesoscale dynamics and cloud physics.

Realistic estimate of background field and observation errors and horizontal correlation of these errors with distance are necessary for correct analysis of rainfall rate. Information from satellites and results of a numerical prediction model are used for objective analysis, done using a method of statistical interpolation. Results show that such an objective analysis is possible as the quality of short-term precipitation forecasts, used as background field, is good.

A best-fit curve relating measured voltage to drop size is compared to a linear interpolation between calibration points of a disdrometer. Both are used in the calculation of drop size distributions in an experiment of the Malaysian Meteorologial Service and the University of Toronto in Penang, Malyasia. Results showed that raindrop spectra were affected by the best-fit curve of the calibration data. Rain rate and radar reflectivity were unaffected.

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission Global Validation Program provides a unique opportunity to compare radar datasets from different sites, because they are analyzed in a relatively uniform procedure. Monthly observed radar reflectivity-rainfall rate ([Z.sub.e]-R) relations for four different sites that are surrounded by tipping bucket gauge networks (Melbourne, Florida; Houston, Texas; Darwin, Australia; and Kwajalein Atoll, Republic of Marshall Islands) were derived. The radar and gauge data from all sites are controlled for quality using the same algorithms, which also include an automated procedure to filter unreliable rain gauge data upon comparison with radar data. The relations are generated by two different methods. The first method is based on using a power law [Z.sub.e]-R with a fixed exponent of 1.4, and the second is based on matching unconditional probabilities of rain rates as measured by the gauge to radar-observed reflectivities and is known as the window probability matching method (WPMM). Both methods tune the radar observations to a network of quality-controlled gauges to adjust the total monthly rainfall to match the gauges. Separate relations are generated for convective and stratiform rain, as classified by the horizontal reflectivity structure. In the WPMM-based [Z.sub.e]-R relations, a given [Z.sub.e] was matched to a much lower R in convective rainfall than in stratiform rainfall. These relations were found to be curved lines in log--log space rather than a straight-line power law. The WPMM-based [Z.sub.e]-R curves demonstrated systematic variation between the convective and stratiform rain, but the power law--based [Z.sub.e]-R curves showed no systematic trend. The systematic variation in the relations shown here contradicts previous findings in which the classification is based only on the existence or nonexistence of brightband signature. The latter indicates a higher reflectivity in stratiform rain as compared with that in convective rain, for a given rain rate. Recent studies, based on disdrometer data, suggest that during a typical event there are three principal types of rain (convective, transition, and stratiform), each characterized by a different type of Z-R relation. The current study suggests that to distinguish each type, both the horizontal and the vertical reflectivity field structure should be analyzed.

This study develops a Gaussian mixture rainfall-rate estimator (GMRE) for polarimetric radar-based rainfall-rate estimation, following a general framework based on the Gaussian mixture model and Bayes least squares estimation for weather radar-based parameter estimations. The advantages of GMRE are 1) it is a minimum variance unbiased estimator; 2) it is a general estimator applicable to different rain regimes in different regions; and 3) it is flexible and may incorporate/exclude different polarimetric radar variables as inputs. This paper also discusses training the GMRE and the sensitivity of performance to mixture number. A large radar and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign is used to evaluate the GMRE approach. Results indicate that the GMRE approach can outperform existing polarimetric rainfall techniques optimized for this JPOLE dataset in terms of bias and root-mean-square error. [ABSTRACT FROM AUTHOR]

Radar measurements are useful for determining rainfall rates because of their ability to cover large areas. Unfortunately, estimating rainfall rates from radar reflectivity data alone is prone to errors resulting from variations in drop size distributions, precipitation types, and other physics that cannot be represented in a simple, one-dimensional Z- R relationship. However, improving estimates is possible by utilizing additional inputs, thereby increasing the dimensionality of the model. The main purpose of this study is to determine the value of surface observations for improving rainfall-rate estimation. This work carefully designed an artificial neural network to fit a model that would relate radar reflectivity, surface temperature, humidity, pressure, and wind to observed rainfall rates. Observations taken over 13 years from the Oklahoma Mesonet and the KTLX WSR-88D radar near Oklahoma City, Oklahoma, were used for the training dataset. While the artificial neural network underestimated rainfall rates for higher reflectivities, it did have an overall better performance than the best-fit Z- R relation. Most importantly, it is shown that the surface data contributed significant value to an unaugmented radar-based rainfall-rate estimation model. [ABSTRACT FROM AUTHOR]

Seasonal and spatial variation of the vertical gradient of rainfall rate was investigated using global precipitation data observed by the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite. The vertical gradient was rendered by features of downward decreasing (DD) or downward increasing (DI) rainfall rate in the lower part of the profile. The DD profiles dominated tropical interior landmasses such as Africa and the Brazilian Plateau in summer. The DI profiles were observed over land in winter and over ocean except for regions with very little rainfall. In addition, DI profiles appeared during the height of the wet season even over the tropical landmasses, such as the mature monsoon period over inland India and over the Amazon River basin. Individual precipitation systems were also investigated in terms of their areally averaged DD and DI characteristics mainly over India. Deep (shallow) profiles tended to be DD (DI) for all seasons except the premonsoon season. As the rain area increased, the vertical gradient of rainfall rate decreased (DD tendency). Embedded in the dominant DD signature for deep storms, deep but significant DI profiles were observed in every month. They characterized the precipitation in the premonsoon season. More than half of the mesoscale/ synoptic-scale systems (rain areas >104 km2) having the significant DD or DI regions had both of them as part of their slant cores. The vertical gradients for these systems had a similar trend for both their stratiform and convective parts. During the mature period of the southwest monsoon, the number of small systems that were DI and widespread systems with moderate vertical gradient increased. [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]

Tropical cyclones, also known as typhoons or hurricanes, are among the most devastating events in nature and often strike the western North Pacific region (including the Philippines, Taiwan, Japan, Korea, China, and others). This paper focuses on addressing the rainfall retrieval problem for quantitative precipitation forecast during tropical cyclones. In this study, Special Sensor Microwave Imager (SSM/I) data and Water Resources Agency (WRA) measurements of Taiwan were used to quantitatively estimate precipitation over the Tanshui River basin in northern Taiwan. Various retrievals for the rainfall rate over land are compared by five methods/techniques. They are the single-channel regression, multichannel linear regressions (MLR), scattering index over land approach (SIL), support vector regression (SVR), and the proposed SIL-SVR. This study collected 70 typhoons affecting the studied watershed over the past 12 years (1997-2008). The measurements of the SSM/I satellite comprise the brightness temperatures at 19.35, 22.23, 37.0, and 85.5 GHz. Overall, the results showed the approaches using the SVR and conjoined SVR and SIL performed better than regression and SIL methods according to their performances of the root-mean-square error (RMSE), bias ratio, and equitable threat score (ETS). [ABSTRACT FROM AUTHOR]

Polarimetric radars provide measurements that describe the shape and dimensions of hydrometeors and are unaffected by calibration, attenuation, and the presence of ice. These measurements can potentially lead to a more detailed description of hydrometeors and to an improvement in quantitative rainfall rate estimation. The authors present an algorithm that exploits polarimetric measures for rain-rate estimation and investigate its application in a real-time framework by using measurements from the C-band polarimetric radar at Monte Settepani in Savona, Italy. It is based on a flowchart decision tree that allows the use of the best rain-rate retrieval algorithm, depending on the value of polarimetric variables. The methodology was applied to a real-time framework for more than a year, and the results were presented for all the significant events observed during the test period. To evaluate the performance of the algorithm, a comparison was made with rain gauge observation from a dense regional network. The performances of the algorithm were compared with those obtained by standard operational Z–R formulations to evaluate the benefit of this approach for operational applications. [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]

Current attempts to measure short-term (<1 month) rainfall accumulations using spaceborne radiometers are characterized by large sampling errors associated with low observation frequencies for any single point on the globe (from two to eight measurements per day). This degrades the value of spaceborne rainfall retrievals for the monitoring of surface water and energy balance processes. Here a data assimilation system, based on the assimilation of surface L-band brightness temperature (T[sub B] ) observations via the ensemble Kalman filter (EnKF), is introduced to correct for the impact of poorly sampled rainfall on land surface model predictions of root-zone soil moisture and surface energy fluxes. The system is evaluated during the period from 1 April 1997 to 31 March 1998 over two sites within the U.S. Southern Great Plains. This evaluation includes both a data assimilation experiment, based on synthetically generated T[sub B] measurements, and the assimilation of real T[sub B] observations acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97). Results suggest that the EnKF-based assimilation system is capable of correcting a substantial fraction (>50%) of model error in root-zone (40 cm) soil moisture and latent heat flux predictions associated with the use of temporally sparse rainfall measurements as forcing data. Comparable gains in accuracy are demonstrated when actual T[sub B] measurements made during the SGP97 experiment are assimilated. [ABSTRACT FROM AUTHOR]

In this paper, a variational data assimilation approach is used to assimilate the rain rate (RR) data together with precipitable water (PW) measurements from the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) (4–5 January 1989; IOP-4 cyclone). The PW and RR, which are assimilated into the Pennsylvania State University–NCAR Mesoscale Model version 5 (MM5), are computed from the Special Sensor Microwave/Imager (SSM/I) raw data—brightness temperatures—via a statistical regression method. The SSM/I-derived RR and PW at 0000 UTC and/or 0930 UTC are assimilated into the MM5. The data at 2200 UTC are used for verification of the prediction results. Numerical experiments are performed using the MM5. Two horizontal resolutions of 50 km and 25 km are used in the authors’ studies. Comparisons are made between the experiments with and without SSM/I-measured PW and RR observations. Results from these experiments showed the following. 1) The MM5 simulated a well-behaved but slightly less intense, position-shifted cyclogenesis episode based on the NCEP analysis enhanced with only radiosonde and surface observations through a Cressman-type objective analysis. 2) The satellite-derived PW and RR observations were assimilated successfully into the MM5 model by a variational method. The cost function that measures the distance between the model-predicted and the observed PW and RR decreased by about one order of magnitude. 3) Assimilation of PW and RR significantly improved the cyclone prediction, reflected mostly in the cyclone’s track, the associated frontal structure and the associated precipitation along the front. The model’s spinup problem during the simulation was greatly reduced after assimilating the PW and RR information into the model initial conditions. 4) Sensitivity experiments of RR assimilation indicated that the impact on the results of RR assimilation was less sensitive to errors in the... [ABSTRACT FROM AUTHOR]