Total Rain Volume Research Articles

Spatiotemporal variability of the rainy season in the Yucatan Peninsula

AbstractThe rainfall regime is a critical factor in the Yucatan Peninsula, as the spatial and multiannual variability of rainfall is a major concern, particularly for crops. Variability in the rainy season was examined considering the onset and demise of the annual rainy season, the total rain volume, the rainfall season duration and the intense precipitation events recorded in meteorological stations (1978–2020). We analysed individual time series and calculated the long‐term trend. Additionally, we explored the relationship between each summer rainfall characteristic and several oceanographic indices using multivariate techniques. We also developed a Trans‐Isthmic Index from the relationship between the El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation. This index allows for determining the effect of the overall influence of the ocean on climate. The timeseries analysis revealed a high interannual variability and long‐term positive trends concerning the duration of the rainy season with earlier onset and later demise, and the total rainfall volume and also a positive trend for the occurrence of heavy precipitation suggesting a shift in intra‐annual patterns. Spatially, the analysis revealed clusters of stations with a similar variation, probably related to the AMO, NIÑO3.4 or TII indices. The spatial pattern was confirmed by analysing CHIRPS gridded precipitation data. Our results show that wetter conditions are associated with lower temperatures in the equatorial Pacific and warmer conditions in the Atlantic.

MJO-Induced Warm Pool Eastward Extension Prior to the Onset of El Niño: Observations from 1998 to 2019

Abstract The Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO) are the two most important tropical phenomena that affect global weather and climate on intraseasonal and interannual time scales. Although they occur on different time scales, the MJO-induced sea surface temperature (SST) anomalies over the equatorial Pacific have spatial scales similar to SST anomalies prior to El Niño. This study aims to address the question of whether the MJO plays an important role in the warm pool eastward extension (WPEE) leading up to El Niño. We use over 20 years of satellite observations, including optimum interpolated SST, TRMM-GPM precipitation, and the cross-calibrated multiplatform (CCMP) surface winds from 1998 to 2019, to quantify the spatial structure and duration of the MJO-induced warm SST anomalies over the equatorial Pacific (10°S–10°N, 130°E–180°). The intensity of the MJO is measured by the total rain volume and average surface westerly wind speed throughout its convectively active phase. Results show that 1) 61% of the 98 MJO events induced a WPEE over 1000–3000 km along the equator, which can last beyond 15–30 days after the MJO precipitation ended; 2) the MJO events prior to El Niño are generally stronger and produce significant WPEE far beyond its annual cycle and increasing SST warming in the Niño-3.4 region; 3) consecutive MJO events can produce much stronger WPEE prior to El Niño, which are observed in all El Niño events from 1998 to 2019; and 4) more frequent and stronger MJO-induced WPEE occurs in March–May than other seasons. These results can help better understand the MJO–ENSO interaction and, ultimately, improve the prediction of El Niño onset.

Likelihood and Bayesian Inference in the Lomax Distribution under Progressive Censoring

The Lomax distribution has been used as a statistical model in several fields, especially for business failure data and reliability engineering. Accurate parameter estimation is very important because it is the base for most inferences from this model. In this paper, we shall study this problem in detail. We developed several points and interval estimators for the parameters of this model assuming the data are type II progressively censored. Specifically, we derive the maximum likelihood estimator and the associated Wald interval. Bayesian point and interval estimators were considered. Since they can't be obtained in a closed form, we used a Markov chains Monte Carlo technique, the so called the Metropolis – Hastings algorithm to obtain approximate Bayes estimators and credible intervals. The asymptotic approximation of Lindley to the Bayes estimator is obtained for the present problem. Moreover, we obtained the least squares and the weighted least squares estimators for the parameters of the Lomax model. Simulation techniques were used to investigate and compare the performance of the various estimators and intervals developed in this paper. We found that the Lindley's approximation to the Bayes estimator has the least mean squared error among all estimators and that the Bayes interval obtained using the Metropolis – Hastings to have better overall performance than the Wald intervals in terms of coverage probabilities and expected interval lengths. Therefore, Bayesian techniques are recommended for inference in this model. An example of real data on total rain volume is given to illustrate the application of the methods developed in this paper.

THE COUPLING BET WEEN CONVECTIVE VARIABILITY AND LARGE-SCALE FLOW PATTERNS OBSERVED DURING PISTON 2018-2019

AbstractThe Propagation of Intraseasonal Oscillations (PISTON) field campaign took place in the waters of the western tropical North Pacific during the late-summer and early-fall of 2018 and 2019. During both research cruises, the Colorado State University SEA-POL polarimetric C-Band weather radar obtained continuous 3D measurements of oceanic precipitation systems. This study provides an overview of the variability in convection observed during the PISTON cruises, and relates this variability to large-scale atmospheric conditions. Using an objective classification algorithm, precipitation features are identified and labeled by their size (isolated/sub-MCS/MCS) and degree of convective organization (nonlinear/linear). It is shown that although large mesoscale convective systems (MCS’s) occurred infrequently (present in 13% of radar scans), they contributed a disproportionately large portion (56%) of the total rain volume. Conversely, small isolated features were present in 91% of scans, yet these features contributed just 11% of the total rain volume, with the bulk of the rainfall owing to warm rain production. Convective rain rates and 30-dBZ echo top heights increased with feature size and degree of organization. MCS’s occurred more frequently in periods of low-level southwesterly winds, and when low-level wind shear was enhanced. By compositing radar and sounding data by phases of easterly waves (of which there were several in 2018), troughs are shown to be associated with increased precipitation and a higher relative frequency of MCS feature occurrence, while ridges are shown to be associated with decreased precipitation and a higher relative frequency of isolated convective features.

Global Survey of Precipitation Properties Observed during Tropical Cyclogenesis and Their Differences Compared to Nondeveloping Disturbances

Abstract This study evaluates precipitation properties involved in tropical cyclogenesis by analyzing a multiyear, global database of passive microwave overpasses of the pregenesis stage of developing disturbances and nondeveloping disturbances. Precipitation statistics are quantified using brightness temperature proxies from the 85–91-GHz channels of multiple spaceborne sensors, as well as retrieved rain rates. Proxies focus on the overall raining area, areal coverage of deep convection, and the proximity of precipitation to the disturbance center. Of interest are the differences in those proxies for developing versus nondeveloping disturbances, how the properties evolve during the pregenesis stage, and how they differ globally. The results indicate that, of all of the proxies examined, the total raining area and rain volume near the circulation center are the most useful precipitation-related predictors for genesis. The areal coverage of deep convection also differentiates developing from nondeveloping disturbances and, similar to the total raining area, generally also increases during the pregenesis stage, particularly within a day of genesis. As the threshold convective intensity is increased, pregenesis cases are less distinguishable from nondeveloping disturbances. Relative to the western Pacific and Indian Oceans, the Atlantic and eastern North Pacific Oceans have less precipitation and deep convection observed during genesis and the smallest differences between developing and nondeveloping disturbances. This suggests that the total raining area and areal coverage of deep convection associated with tropical disturbances are better predictors of tropical cyclogenesis fate in the Pacific and Indian Oceans than in the Atlantic and eastern North Pacific.

The Influence of Aerosols and Meteorological Conditions on the Total Rain Volume of the Mesoscale Convective Systems Over Tropical Continents

AbstractThis study provides an observational assessment of the variations of the total rain volume (TRV) with aerosols through the entire lifetime of mesoscale convective systems (MCSs) over tropics. Using 70,000 MCSs' samples, we show that TRV increases with aerosols from clean to moderately heavy polluted conditions (aerosol optical depth [AOD] ~ 0.0–0.4). TRV decreases when AOD exceeds 0.5. The TRV change with AOD is strongest under favorable meteorological conditions, such as high total precipitable water (45–75 kg/m2), high convective available potential energy (1,200–2,400 J/kg), and intermediate vertical wind shear (9–21 × 10−4/s). TRV of MCSs increases from 2 to 4 km3 (rain depth ~ 20–40 mm) when AOD < 0.15 or >0.5, to more than 12 km3 (~120 mm) when 0.2 < AOD < 0.4 under above the mentioned optimal meteorological conditions. The basic response of TRV to aerosol concentrations is similar under all the meteorological conditions and during all stages of the MCS lifecycle.

Dual-Polarization Radar Rainfall Estimation over Tropical Oceans

AbstractDual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Zdr; specific differential phase Kdp; reflectivity Zh; and specific attenuation Ah. When compared with continental or coastal convection, tropical oceanic Zdr and Kdp values were more often of low magnitude (<0.5 dB, <0.3° km−1) and Zdr was lower for a given Kdp or Zh, consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(Kdp), R(Ah), R(Kdp, ζdr), R(z, ζdr), and R(Ah, ζdr), which use linear versions of Zdr and Zh, namely ζdr and z. Except for R(Kdp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(Kdp, ζdr), followed by R(Ah, ζdr) and R(z, ζdr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζdr), R(Kdp), and R(Kdp, ζdr) depending on Zdr > 0.25 dB and Kdp > 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(Kdp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R < 5 mm h−1, Zdr < 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(Kdp, ζdr) was used more often, R(z, ζdr) was used less often, and the blended algorithm became increasingly more accurate than R(z).

Possible impacts of a changing climate on intense Ligurian Sea rainfall events

ABSTRACTThe region near the Ligurian Sea in northwestern Italy occasionally experiences extreme rainfall events that can lead to catastrophic flooding. These events are often caused by back‐building mesoscale convective systems whose occurrence peaks during the autumn. Rapid warming of the Mediterranean Sea in recent decades has raised concerns that the warmer water may change the frequency or intensity of these rainfall events. To gain insight into possible impacts of the warming climate, surface wind and precipitation output from 12‐km horizontal grid spacing EXPRESS‐Hydro simulations of past and future climate are used to examine trends in the frequency of strong convergence and heavy rainfall events during both the past and the future climate. Strong convergence events are not found to increase in frequency in the future climate, but the rainfall associated with these events does increase. The area that experiences rainfall above a specified threshold along with the duration over which such conditions are experienced increases, especially during the autumn when increases are statistically significant. Peak 3‐hourly rainfall at a grid point does not increase in the future climate but total rain volume associated with these events does, reflecting the larger areal coverage and duration of the events.

Drop Size Distributions and Radar Observations of Convective and Stratiform Rain over the Equatorial Indian and West Pacific Oceans

Abstract Two-dimensional video disdrometer (2DVD) data were analyzed from two equatorial Indian (Gan) and west Pacific Ocean (Manus) islands where precipitation is primarily organized by the intertropical convergence zone and the Madden–Julian oscillation (MJO). The 18 (3.5) months of 2DVD data from Manus (Gan) Island show that 1) the two sites have similar drop size distribution (DSD) spectra of liquid water content, median diameter, rain rate R, radar reflectivity z, normalized gamma number concentration Nw, and other integral rain parameters; 2) there is a robust Nw-based separation between convective (C) and stratiform (S) DSDs at both sites that produces consistent separation in other parameter spaces. The 2DVD data indicate an equatorial, maritime average C/S rainfall accumulation fraction (frequency) of 81/19 (41/59) at these locations. It is hypothesized that convective fraction and frequency estimates are slightly higher than previous radar-based studies, because the ubiquitous weak, shallow convection (<10 mm h−1) characteristic of the tropical warm pool is properly resolved by this high-resolution DSD dataset and identification method. This type of convection accounted for about 30% of all rain events and 15% of total rain volume. These rain statistics were reproduced when newly derived C/S R(z) equations were applied to 2DVD-simulated reflectivity. However, the benefits of using separate C/S R(z) equations are only realizable when C/S partitioning properly classifies each rain type. A single R(z) relationship fit to all 2DVD data yielded accurate total rainfall amounts but overestimated (underestimated) the stratiform (convective) rain fraction by ±10% and overestimated (underestimated) stratiform (convective) rain accumulation by +50% (−15%).

Flash floods in the Sahara: a case study for the 28 January 2013 flood in Qena, Egypt

Understanding the torrential rainfall and its consequent surface runoff in the Sahara is a crucial issue for better flood protection and water management plans. This is often hampered by the lack of appropriate in situ measurements. Even now the satellite-derived rainfall suffers from great uncertainty. Thus, we adjusted the data obtained from real-time satellite rainfall coverage (HYDIS) using the in situ observed rainfall (Robs). Hydro-morphological parameters were then integrated with the empirical curve number (CN) approach to estimate the surface runoff in Qena, Egypt during the 28 January 2013 flash flood event. We deduced that the study area received a total precipitation (∑Rcum) of ∼35.6 × 106 [mm] and a total rain volume (∑Rvol) of ∼88.9 × 109 [m3] mainly from wadi Qena (89.8%). The majority of the rainfalls fell at light intensity (<2.5 [mm hr−1]). The estimated total surface runoff (∑Qsur) was 26.5 × 106 mm and the total runoff volume (∑Qvol) was 66.2 × 106 [m3]. The total surface transmission losses (∑Tlos) were calculated as 9.1 × 106 [mm], which represents about 25.6% of the total precipitation (∑Rcum) and creates substantial opportunities for alluvial aquifer recharge. The total surface runoff (∑Qsur) and flood magnitude were generally low, therefore, flood influences were restricted to the destruction of some roads in Qena but no fatalities were involved, nevertheless. Most of the running water was contained by El Sail Canal and poured into the Nile River. It is expected that the applied method in this study will be helpful for our understanding and quantification of flood hydrology and contribute to better risk management plan in the arid and hyper-arid regions.

Analysis of the flash flood occurred on 18 January 2010 in wadi El Arish, Egypt (a case study)

Wadi El Arish is the largest drainage system in Sinai Peninsula, Egypt. The wadi was subjected to severe thunderstorms on 17 and 18 January 2010 followed by extreme violent flood that had not been known in Sinai since 1980. The flood claimed six victims, injured tens and devastated vital infrastructure and hundreds of houses. Hydrologic characteristics of the wadi are not fully understood due to the great lack of the detailed hydro-meteorological information. Hence, remote sensing and geographic information system (GIS) techniques were widely used to provide better spatial understanding of storm rain characteristics and drainage basin response. The study was based on the analysis of the TRMM images and the averaged six hourly HYDIS data for storm rain analysis. Hydrological model was constructed using ASTER digital elevation models (DEMs). The empirical black model box was used depending on the curve number approach to predict stream runoff. Landcover and hydrological soil groups were identified from the Landsat ETM+ mosaic using multispectral supervised classification. The curve numbers were adjusted to ARC I (dry condition) as the total rain in five-day period preceding the storm was very low (≤5 mm). The study deduced that the wadi received a total rain volume of 665.4 × 106 m3. Most of the rain fell on 17 January with an intensity ranged between 4–8 mm/hr. The estimated total surface runoff Q was 123.3 × 103 mm and the total runoff volume was 124 × 106 m3 that constitutes 18.8% of the total rain volume. The results are in agreement with the observed values in the wadi, and highly required for many applications related to water harvesting and flood protection studies.

Seasonal Tropical Cyclone Rain Volumes over Australia

Abstract For a continent as dry as Australia, where water is a valuable resource, it is important to understand the sources of rainfall. The volume of water contributed by tropical cyclones (TCs) during the November–April season is investigated using 42 seasons of TC and rainfall data. The seasonal total TC rain volume (SRV) ranges from a minimum of 34.2 km3 in 1987/88 to a maximum of 564.4 km3 in 2000/01, with a long-term mean of 198.6 ± 107.4 km3. In terms of mean percentage, TCs contribute 7.6% to the seasonal total rain volume over Australia. The number of landfalling TCs and the number of TCs that individually produce more than the mean individual TC rain volume (25.7 km3) during a season are significant influences on the SRV. The TCs passing near the coast without landfalling have little impact on the SRV. The two parameters that correlate best with SRV are the total time spent over land by TCs during a season and the total land area covered by TCs during a season (correlation coefficients of 0.79 and 0.84, respectively). Although the highest SRVs occur almost exclusively during La Niña and neutral seasons, there is a mixture of ENSO seasons corresponding to the lowest SRVs. In general, the large interannual variability, even within a particular ENSO class, indicates that caution should be used when attempting to relate SRV to the phase of ENSO.

Dynamics of the chemical composition of rainwater throughout Hurricane Irene

Abstract. Sequential sampling of rainwater from Hurricane Irene was carried out in Wilmington, NC, USA on 26 and 27 August 2011. Eleven samples were analyzed for pH, major ions (Cl−, NO3−, SO42−, Na+, K+, Mg2+, Ca2+, NH4+), dissolved organic carbon (DOC) and hydrogen peroxide (H2O2). Hurricane Irene contributed 16% of the total rainwater and 18% of the total chloride wet deposition received in Wilmington NC during all of 2011. This work highlights the main physical factors influencing the chemical composition of tropical storm rainwater: wind speed, wind direction, back trajectory and vertical mixing, time of day and total rain volume. Samples collected early in the storm, when winds blew out of the east, contained dissolved components indicative of marine sources (salts from sea spray and low DOC). The sea-salt components in the samples had two maxima in concentration during the storm the first of which occurred before the volume of rain had sufficiently washed out sea salt from the atmosphere and the second when back trajectories showed large volumes of marine surface air were lifted. As the storm progressed and winds shifted to a westerly direction, the chemical composition of the rainwater became characteristic of terrestrial storms (high DOC and NH4+ and low sea salt). This work demonstrates that tropical storms are not only responsible for significant wet deposition of marine components to land, but terrestrial components can also become entrained in rainwater, which can then be delivered to coastal waters via wet deposition. This study also underscores why analysis of one composite sample can lead to an incomplete interpretation of the factors that influence the chemically divergent analytes in rainwater during extreme weather events.

On the quantification of oceanic rainfall using spaceborne sensors

Much of our knowledge about oceanic rainfall comes from spaceborne sensors. These sensors provide direct or indirect information used for precipitation retrievals through various algorithms. A thorough understanding of rain frequency and intensity and its regional distribution, which is especially important in a warming climate, requires an evaluation of the performance of rain‐measuring sensors and identification of strengths and limitations offered by each sensor. The Tropical Rainfall Measuring Mission (TRMM) has enabled significant advancement in quantification of moderate to intense rainfall. However, a common limitation of the current suite of rain‐measuring sensors is their lack of sensitivity to light rainfall, especially over subtropical and high‐latitude oceans. Among various spaceborne sensors, CloudSat enables superior retrieval of light rainfall and drizzle. By using 3 years (2007–2009) of rainfall data from CloudSat and the precipitation radar aboard TRMM, it was determined that the quasi‐global (60°S–60°N) oceanic mean rain rate is about 3.05 mm/d, considerably larger than that obtained from any individual sensor product. In the deep tropics, especially within 20°S–20°N, the sensors show the highest agreement, with a large fraction of total rain volume captured by the majority of sensors. However, toward higher latitudes and within the subtropical high‐pressure regions, a significant fraction of rainfall, which can exceed 50% or more of total rain volume, is missed by the majority of the sensors.

Estimation of Rain Intensity Spectra over the Continental United States Using Ground Radar–Gauge Measurements

Abstract A high-resolution surface rainfall product is used to estimate rain characteristics over the continental United States as a function of rain intensity. By defining data at 4-km horizontal resolutions and 1-h temporal resolutions as an individual precipitating or nonprecipitating sample, statistics of rain occurrence and rain volume including their geographical and seasonal variations are documented. Quantitative estimations are also conducted to evaluate the impact of missing light rain events due to satellite sensors’ detection capabilities. It is found that statistics of rain characteristics have large seasonal and geographical variations across the continental United States. Although heavy rain events (&amp;gt;10 mm h−1) only occupy 2.6% of total rain occurrence, they may contribute to 27% of total rain volume. Light rain events (&amp;lt;1.0 mm h−1), occurring much more frequently (65%) than heavy rain events, can also make important contributions (15%) to the total rain volume. For minimum detectable rain rates setting at 0.5 and 0.2 mm h−1, which are close to sensitivities of the current and future spaceborne precipitation radars, there are about 43% and 11% of total rain occurrence below these thresholds, and they respectively represent 7% and 0.8% of total rain volume. For passive microwave sensors with their rain pixel sizes ranging from 14 to 16 km and the minimum detectable rain rates around 1 mm h−1, the missed light rain events may account for 70% of rain occurrence and 16% of rain volume. Statistics of rain characteristics are also examined on domains with different temporal and spatial resolutions. Current issues in estimates of rain characteristics from satellite measurements and model outputs are discussed.

The Distribution of Rainfall over Oceans from Spaceborne Radars

Abstract A combination of rainfall estimates from the 13.8-GHz Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the 94-GHz CloudSat Cloud Profiling Radar (CPR) is used to assess the distribution of rainfall intensity over tropical and subtropical oceans. These two spaceborne radars provide highly complementary information: the PR provides the best information on the total rain volume because of its ability to estimate the intensity of all but the lightest rain rates while the CPR’s higher sensitivity provides superior rainfall detection as well as estimates of drizzle and light rain. Over the TRMM region between 35°S and 35°N, rainfall frequency from the CPR is around 9%, approximately 2.5 times that detected by the PR, and the CPR estimates indicate a contribution by light rain that is undetected by the PR of around 10% of the total. Stratifying the results by total precipitable water (TPW) as a proxy for rainfall regime indicates dramatic differences over stratus-dominated subsidence regions, with nearly 20% of the total rain occurring as light rain. Over moist tropical regions, the CPR substantially underestimates rain from intense convective storms because of large attenuation and multiple-scattering effects while the PR misses very little of the total rain volume because of a lower relative contribution from light rain. Over low-TPW regions, however, inconsistencies between estimates from the PR and the CPR point to uncertainties in the algorithm assumptions that remain to be understood and addressed.

The Impact of Different WRF Model Physical Parameterizations and Their Interactions on Warm Season MCS Rainfall

Abstract In recent years, a mixed-physics ensemble approach has been investigated as a method to better predict mesoscale convective system (MCS) rainfall. For both mixed-physics ensemble design and interpretation, knowledge of the general impact of various physical schemes and their interactions on warm season MCS rainfall forecasts would be useful. Adopting the newly emerging Weather Research and Forecasting (WRF) model for this purpose would further emphasize such benefits. To pursue this goal, a matrix of 18 WRF model configurations, created using different physical scheme combinations, was run with 12-km grid spacing for eight International H2O Project (IHOP) MCS cases. For each case, three different treatments of convection, three different microphysical schemes, and two different planetary boundary layer schemes were used. Sensitivity to physics changes was determined using the correspondence ratio and the squared correlation coefficient. The factor separation method was also used to quantify in detail the impacts of the variation of two different physical schemes and their interaction on the simulated rainfall. Skill score measures averaged over all eight cases for all 18 configurations indicated that no one configuration was obviously best at all times and thresholds. The greatest variability in forecasts was found to come from changes in the choice of convective scheme, although notable impacts also occurred from changes in the microphysics and planetary boundary layer (PBL) schemes. Specifically, changes in convective treatment notably impacted the forecast of system average rain rate, while forecasts of total domain rain volume were influenced by choices of microphysics and convective treatment. The impact of interactions (synergy) of different physical schemes, although occasionally of comparable magnitude to the impacts from changing one scheme alone (compared to a control run), varied greatly among cases and over time, and was typically not statistically significant.

A Modified ATI Technique for Nowcasting Convective Rain Volumes over Areas

This paper explores the applicability of the area-time-integral (ATI) technique for the estimation of the growth portion only of a convective storm (while the rain volume is computed using the entire life history of the event) and for nowcasting the total rain volume of a convective system at the stage of its maximum development. For these purposes, the ATIs were computed from the digital radar data (for 1981-1982) from the North Dakota Cloud Modification Project, using the maximum echo area (ATIA) no less than 25 dBz, the maximum reflectivity, and the maximum echo height as the end of the growth portion of the convective event. Linear regression analysis demonstrated that correlations between total rain volume or the maximum rain volume versus ATIA were the strongest. The uncertainties obtained were comparable to the uncertainties which typically occur in rain volume estimates obtained from radar data employing Z-R conversion followed by space and time integration. This demonstrates that the total rain volume of a storm can be nowcasted at its maximum stage of development.

Infrared and Visible Satellite Rain Estimation. Part II: A Cloud Definition Approach

This study examines the relationships between satellite infrared clouds and rainfall, and infrared-threshold visible clouds and rainfall. Clouds are defined by the outline of the 253 K isotherm. Cloud infrared area was highly correlated with rain area (ρ = 0.85) and with volume rainrate (ρ = 0.81). It was poorly correlated with mean cloud rainrate (ρ = −0.28). One-parameter models were as effective in explaining the variance of cloud volume rainrate as multiparameter methods, due to the high correlations between visible brightness, mean cloud temperature and cloud area. An exception was found for clouds >10 000 km2, where area and temperature were uncorrelated, and mean temperature was more effective in discriminating among classes of volume rain than was cloud area. Statistical separation of five- of six-volume rain classes was achieved with mean temperature; however, the probability of occurrence of the classes effectively reduced this to a four-class problem. Due to the high correlation between visible brightness and infrared temperature, visible data provided largely redundant information. Using a mean cloud brightness threshold of 148 counts, rain/no-rain separation was effected with a POD, FAR, and CSI of 0.98, 0.13, and 0.86, respectively. An infrared threshold (mean temperature of 241 K) produced statistics of 0.88, 0.07 and 0.83, respectively for the POD, FAR and CSI. The standard deviation of visible counts (used as a measure of cloud structure) was poor in explaining the variance of rainrate, yielding no better than rain/no-rain separation. Time series of the cloud evolution showed that rain volume fluctuations were better “mirrored” by cloud temperature fluctuations than by cloud area. Contrary examples could be found and inconsistency between days was noted. The apportionment of rain volume (assigning rainrates to areas) remained a difficult problem, with significant variability, both within clouds of the same size and between clouds of different size. The coldest 10% cloud area was found to contain 11%–23% of the total rain volume while the coldest 50% area contained 60%–70–. This is in contrast to the rain apportionment used in the Griffith-Woodley Technique.

The Area-Time-Integral Technique to Estimate Convective Rain Volumes over Areas Applied to Satellite Data—A Preliminary Investigation

Early work attempting to apply GOES rapid scan satellite data to a recently developed simple radar technique used to estimate convective rain volumes over areas in a semiarid environment (the northern Great Plains) is described. Called the Area-Time-Integral (ATI) technique, it provides a means of estimating total rain volumes over fixed and floating target areas. The basis of the method is the existence of a strong correlation between the radar echo area coverage integrated over the lifetime of the storm and the radar estimated rain volume. The technique does not require the consideration of the structure of the radar intensities to generate rain volumes. but only the area covered by radar echoes. This fact might reduce the source of errors generated by the structure differences between the radar and the satellite signatures above given thresholds. Satellite and radar data from the 1981 Cooperative Convective Precipitation Experiment (CCOPE) and the North Dakota Cloud Modification Project (NDCMP) are used. Consecutive time steps with both radar reflectivities and satellite (VIS and IR) rapid wan data were considered during the evolution of six convective clusters: three on 12 June, and three on 2 July 1981. Radar echoes with reflectivity values ≥ 25 dBZ were used to define the area of rainfall and the respective digital unit thresholds within the satellite data delineating the rainy part of the cloud area. Correlation of the ATI versus IR digital count values was obtained for every time step and for the storm lifetime, respectively. A comparison of the stepwise evolution of radar parameters such as echo areas maximum echo heights, maximum reflectivities and satellite parameters such as threshold count values and coldest cloud top temperature is presented graphically and reflects the multicell characteristics of the convective clusters. Also, a comparison of radar and satellite parameters for the cluster lifetime is made. Satellite parameters pertaining to the cluster lifetime were derived both dependently and independently of radar data. The main purpose of this investigation is to compute convective rain volume of a convective cluster by application of the ATI technique based only on satellite data. As such, the key element is to determine the ATI from satellite data without consideration of radar data. This is possible if trends of satellite products generated independently are similar to those of satellite products based upon radar observations as done here. A parallel with the two-step techniques generally used to estimate rain volume from satellite data is made. To delineate the rainy part of a cloud area, a regression analysis is used. The regression relate a satellite-independent product to a satellite-dependent product. For a given storm. the satellite-independent product is first computed; then the regression equation gives the ATI, Finally, the rain volume is obtained by using the ATI versus rain volume relationship. By applying the ATI/rain volume relationship to satellite data, the errors generated by the complicated multiple area-volume transform relations am reduced, as similar errors were reduced when the technique was applied to radar data. In addition, a regression analysis gives more accurate estimates than a single threshold when used to delineate an area covered by rain events from an area covered by clouds. The advantages of the ATI technique are based on the fact that the technique operates on a storm lifetime integrated basis, while the previous techniques operate on a time-step basis. The new technique generates only total rain volume estimates (not rain rates). This indeed is a limitation. The analyses of six convective clusters suggest that the extension of the ATI technique using satellite data holds promise.