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NASA NTRS2008

Assessing the Relative Performance of Microwave-based Satellite Rain Rate Retrievals using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗

NASA NTRS1981

A moisture analysis procedure utilizing surface and satellite data

Four analyses have been produced by a moisture analysis procedure which uses satellite, precipitation and surface specific humidity data to augment the standard radiosonde observations (RAOB) of moisture: (1) using RAOB data only, (2) using RAOB data supplemented by data obtained from the surface observations of specific humidity, (3) using RAOB data supplemented only by satellite and precipitation data, and (4) employing all sources of data. Penn State University Mesoscale Model forecasts were performed to determine the effect of the high-resolution moisture analyses. For the data set used, supplementary satellite and rainfall data was found to improve the precipitation forecast more than surface humidity data and permitted the model to produce rain rates during the first four hours of the forecast that were 100% larger and more realistic than those produced without these data.

Wolcott, S. W.↗

NASA NTRS2002

New Insights on Hydro-Climate Feedback Processes over the Tropical Ocean from TRMM

In this paper, we study hydro-climate feedback processes over the tropical oceans, by examining the relationships among large scale circulation and Tropical Rainfall Measuring Mission Microwave Imager-Sea Surface Temperature (TMI-SST), and a range of TRMM rain products including rain rate, cloud liquid water, precipitable water, cloud types and areal coverage, and precipitation efficiency. Results show that for a warm event (1998), the 28C threshold of convective precipitation is quite well defined over the tropical oceans. However, for a cold event (1999), the SST threshold is less well defined, especially over the central and eastern Pacific cold tongue, where stratiform rain occurs at much lower than 28 C. Precipitation rates and cloud liquid water are found to be more closely related to the large scale vertical motion than to the underlying SST. While total columnar water vapor is more strongly dependent on SST. For a large domain, over the eastern Pacific, we find that the areal extent of the cloudy region tends to shrink as the SST increases. Examination of the relationship between cloud liquid water and rain rate suggests that the residence time of cloud liquid water tends to be shorter, associated with higher precipitation efficiency in a warmer climate. It is hypothesized that the reduction in cloudy area may be influenced both by the shift in large scale cloud patterns in response to changes in large scale forcings, and possible increase in the cloud liquid water conversion to rain water in a warmer environment. Results of numerical experiments with the Goddard cloud resolving model to test the hypothesis will be discussed.

Lau, William K. M.↗

NASA NTRS2014

The 183-WSL Fast Rain Rate Retrieval Algorithm. Part II: Validation Using Ground Radar Measurements

The Water vapour Strong Lines at 183 GHz (183-WSL) algorithm is a method for the retrieval of rain rates and precipitation type classification (convectivestratiform), that makes use of the water vapor absorption lines centered at 183.31 GHz of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15-18 and NOAA-19Metop-A satellite series, respectively. The characteristics of this algorithm were described in Part I of this paper together with comparisons against analogous precipitation products. The focus of Part II is the analysis of the performance of the 183-WSL technique based on surface radar measurements. The ground truth dataset consists of 2.5 years of rainfall intensity fields from the NIMROD European radar network which covers North-Western Europe. The investigation of the 183-WSL retrieval performance is based on a twofold approach: 1) the dichotomous statistic is used to evaluate the capabilities of the method to identify rain and no-rain clouds; 2) the accuracy statistic is applied to quantify the errors in the estimation of rain rates.The results reveal that the 183-WSL technique shows good skills in the detection of rainno-rain areas and in the quantification of rain rate intensities. The categorical analysis shows annual values of the POD, FAR and HK indices varying in the range 0.80-0.82, 0.330.36 and 0.39-0.46, respectively. The RMSE value is 2.8 millimeters per hour for the whole period despite an overestimation in the retrieved rain rates. Of note is the distribution of the 183-WSL monthly mean rain rate with respect to radar: the seasonal fluctuations of the average rainfalls measured by radar are reproduced by the 183-WSL. However, the retrieval method appears to suffer for the winter seasonal conditions especially when the soil is partially frozen and the surface emissivity drastically changes. This fact is verified observing the discrepancy distribution diagrams where2the 183-WSL performs better during the warm months, while during the winter time the discrepancies with radar measurements tends to maximum values. A stable behavior of the 183-WSL algorithm is demonstrated over the whole study period with an overall overestimation for rain rates intensities lower than 1 millimeter per hour. This threshold is crucial especially in wintertime where the low precipitation regime is difficult to be classified.

validationradar↗

NASA NTRS2020

Delineation of vulnerable areas to water erosion in a mountain region using SDR-InVEST model: A case study of the Ourika watershed, Morocco

The Ourika watershed is characterized by higher than average flood flows, a rugged topography, a sparse vegetation cover and a friable substrate that makes it vulnerable to soil erosion. Soil is an important non-renewable asset capital, providing several ecosystem services that sustain life through physical and biological regulation processes. The loss, or otherwise, redistribution of biota by soil erosion in the Ourika watershed has received little attention and thus constitutes a key knowledge gap that needs to be addressed. This study quantifies the level of erosion and maps its spatial distribution in the watershed using the Sediment Delivery Ratio (SDR) of the Integrated Valuation of Ecosystem Services and Tradeoffs model (InVEST3.6.0). The SDR maps the overland sediment generation and delivery to the stream at the watershed level using rainfall, soil properties, topography, cover management and cropping practices as predefined set of input variables. Recommended internal model calibration was used and results were compared to similar studies carried out in different parts of Morocco for validation. The results reveal that 93% of the watershed is subject to a strong rainfall aggressiveness greater than 50 MJ mm ha–1 h–1 yr1 under an average rainfall intensity of 541 mm/year, rainfall capacity to initiate the detachment and transport of soil particles by runoff, and only 11.5% of the watershed area is protected with vegetative cover. We find that overall, the Ourika watershed is losing its soil to erosion at an alarming average rate of 258.48 ton.ha–1.yr–1, a rate well above the tolerable threshold of 7ton.ha–1.yr–1. The spatial distribution of this loss shows areas that need to be urgently remedied. The primary factors for soil erosion in the Ourika watershed appear to be related to the aggressive nature of local rainfall and the inherent susceptibility of soils, which can be costly to this cash-crop region. Because rainfall intensity, soil properties, and topography are intrinsic characteristics of the watershed, we suggest that seasonally adapted cropping rotations that maintains conservative practices with a high cover management may reduce soil erosion in the Ourika watershed.

Erosion↗

NASA NTRS1986

Some comments on passive microwave measurement of rain

It is argued that because microwave radiation interacts much more strongly with hydrometeors than with cloud particles, microwave measurements from space offer a significant chance of making global precipitation estimates. Over oceans, passive microwave measurements are essentially attenuation measurements that can be very closely related to the rain rate independently of the details of the drop-size distribution. Over land, scattering of microwave radiation by the hydrometeors, especially in the ice phase, can be used to estimate rainfall. In scattering, the details of the drop-size distribution are very important and it is therefore more difficult to achieve a high degree of accuracy. The SSM/I (Special Sensor Microwave Imager), a passive microwave imaging sensor that will be launched soon, will have dual-polarized channels at 85.5 GHz that will be very sensitive to scattering by frozen hydrometeors. Other sensors being considered for the future space missions would extend the ability to estimate rain rates from space. The ideal spaceborne precipitation-measurement system would use the complementary strengths of passive microwave, radar, and visible/infrared measurements.

Wilheit, Thomas T.↗

NASA NTRS1993

Probability-matched reflectivity-rainfall relations for a hurricane from aircraft observations

The relation between the distribution of equivalent reflectivity, Z(sub e), measured by an airborne C-band radar and that for rain rate, R, measured concurrently by a disdrometer on the same aircraft in the eyewall and outer bands of Hurricane Anita (1977) was investigated using the probability-matching method (PMM). It was found that the pdf of R is much narrower in the outer rainbands of Hurricane Anita than in the eyewall, and that the mean rain rates are also much smaller. It is shown that the use of PMM provides more realistic relations than the conventional power-law Z-R regression relations based upon scatter-plots of disdrometer measurements, showing higher eyewall reflectivities for the same rain rates at rates in excess of 6.3 mm/h.

Marks, Frank D., Jr.↗

NASA NTRS1992

Sulfate reduction and other sedimentary biogeochemistry in a northern New England salt marsh

Sulfate reduction rates, dissolved iron and sulfide concentrations, and titration alkalinity were measured in salt marsh soils along a transect that included areas inhabited by both the tall and short forms of Spartina alterniflora and by Spartina patens. Pore waters were collected with in situ 'sippers' to acquire temporal data from the same location without disturbing plant roots. During 1984, data collected at weekly intervals showed rapid temporal changes in belowground biogeochemical processes that coincided with changes in S. alterniflora physiology. Rates of SO4(-2) reduction increased fivefold (to greater than 2.5 micromol ml(sup -1)d(sup -1)) when plants began elongating aboveground yet decreased fourfold upon plant flowering. This rapid increase in rates of SO4(-2) reduction must have been fueled by dissolved organic matter released from roots only during active growth. Once plants flowered, the supply of oxidants to the soil decreased and sulfide and alkalinity concentrations increased despite decreases in SO4(-2) reduction and increases in SO4(-2):Cl(-) ratios. Sulfide concentrations were highest in soils inhabited by tallest plants. During 1985, S. alterniflora became infested with fly larvae (Chaetopsis apicalis John) and aboveground growth ceased in late June. This cessation was accompanied by decreased rates of SO4(-2) reduction similar to those noted during the previous year when flowering occurred. After the fly infestation, the pore-water chemical profiles of these soils resembled profiles of soils inhabited by the short form of S. alterniflora. The SO4(-2) reduction rates in S. patens soils are the first reported. Rates were similar to those in S. alterniflora except that they did not increase greatly when S. patens was elongating. Tidal and rainfall events produced desiccation-saturation cycles that altered redox conditions in the S. patens soils, resulting in rapid changes in the dissolution and precipitation of iron and in the magnitude and spatial distribution of SO4(-2) reduction.

Hines, Mark E.↗

NASA NTRS2001

Observed and Simulated Radiative and Microphysical Properties of Tropical Convective Storms

Increases in the ice content, albedo and cloud cover of tropical convective storms in a warmer climate produce a large negative contribution to cloud feedback in the GISS GCM. Unfortunately, the physics of convective upward water transport, detrainment, and ice sedimentation, and the relationship of microphysical to radiative properties, are all quite uncertain. We apply a clustering algorithm to TRMM satellite microwave rainfall retrievals to identify contiguous deep precipitating storms throughout the tropics. Each storm is characterized according to its size, albedo, OLR, rain rate, microphysical structure, and presence/absence of lightning. A similar analysis is applied to ISCCP data during the TOGA/COARE experiment to identify optically thick deep cloud systems and relate them to large-scale environmental conditions just before storm onset. We examine the statistics of these storms to understand the relative climatic roles of small and large storms and the factors that regulate convective storm size and albedo. The results are compared to GISS GCM simulated statistics of tropical convective storms to identify areas of agreement and disagreement.

DelGenio, Anthony D.↗

NASA NTRS2007

Managing Uncertainty Due to a Fundamental Error Source Arising from Scatterer Distribution Complexity in Radar Remote Sensing of Precipitation

The assumption that cloud and rain drops are spatially distributed according to a Poisson distribution within a scattering volume probed by a radar being used to estimate precipitation has represented bedrock theory in establishing 'rules of the game' for pulse averaging--the process needed to beat down noise to an acceptable level in the measurement of radar reflectivity factor. Based on relatively recent observations of 'realistic' spatial distributions of hydrometeor scatterers in a cloudy atmosphere motivates a renewed examination of the consequences of using a too simplified assumption underlying volume scattering--particularly in regards to the standard pulse averaging rule. Our investigation addresses two extremes, simple to complex, insofar as allowed for complexities in an underlying scatterer distribution. It is demonstrated that as the spatial distribution ranges from Poisson (a narrow distribution) to multi-fractal (much broader distribution), uncertainty in a measurement increases if the rule for pulse averaging goes unchanged from its Poisson distribution reference county. [A bounded cascade is used for the multi-fractal distribution, a regularly observed distribution vis-a-vis cloud liquid water content.] The resultant measurement uncertainty leads to a fundamental source of error in the estimation of rain rate from radar measurements, one that has been disregarded since the early 1950s when radar sets first began to be used for rainfall measuring. It is shown how this source of error can be 'managed'--under the assumption that number of data analysis experiments would be carried out, experiments involving pulse-by-pulse measurements obtained from a radar set modified to output individual pulses of reflectivity factor. For practical applications, a new parameter called normalized k-sample intensity invariance is developed to enable defining the required pulse average count according to a preferred degree of uncertainty.

Smith, Eric A.↗

NASA NTRS2014

Evaluation of the New Version of the Laser-Optical Disdrometer, OTT Parsivel2

A comparative study of raindrop size distribution measurements has been conducted at NASA's Goddard Space Flight Center where the focus was to evaluate the performance of the upgraded laser-optical OTT Particle Size Velocity (Parsivel2; P2) disdrometer. The experimental setup included a collocated pair of tipping-bucket rain gauges, OTT Parsivel (P1) and P2 disdrometers, and Joss-Waldvogel (JW) disdrometers. Excellent agreement between the two collocated rain gauges enabled their use as a relative reference for event rain totals. A comparison of event total showed that the P2 had a 6%absolute bias with respect to the reference gauges, considerably lower than the P1 and JW disdrometers. Good agreement was also evident between the JW and P2 in hourly raindrop spectra for drop diameters between 0.5 and 4 mm. The P2 drop concentrations mostly increased toward small sizes, and the peak concentrations were mostly observed in the first three measurable size bins. The P1, on the other hand, underestimated small drops and overestimated the large drops, particularly in heavy rain rates. From the analysis performed, it appears that the P2 is an improvement over the P1 model for both drop size and rainfall measurements. P2 mean fall velocities follow accepted terminal fall speed relationships at drop sizes less than 1 mm. As a caveat, the P2 had approximately 1ms21 slower mean fall speed with respect to the terminal fall speed near 1 mm, and the difference between the mean measured and terminal fall speeds reduced with increasing drop size. This caveat was recognized as a software bug by the manufacturer and is currently being investigated.

disdrometers↗

NASA NTRS2010

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Wolff, David B.↗

NASA NTRS2011

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗

NASA NTRS2000

Quantitative Estimation of Some Possible Errors Associated With TRMM GV Monthly Rain Maps and a Scheme for Spatial Averaging of Radar Accumulations

The Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) rain products are generated from ground-based radar observations using a bulk adjusted FACE radar reflectivity (Ze) - rain rate (R) relationship. Monthly Ze-R relationships are obtained by extracting the Ze data with a horizontal resolution of 2 km from an altitude of 1.5 km (from Constant Altitude PPIS, CAPPIS) over the locations of the gauges. The gauge and radar data are merged, and a quality control procedure is applied that eliminates poorly correlated gauge-radar data. Bulk adjusted Ze-R relations are then derived from this quality-controlled merged data base. These Z-R relations are applied to the base scan reflectivity to produce rain maps. The present study examines the effect of these different planes on the total accumulations from the radar and the associated effect on the bulk adjustment coefficient. In order to evaluate the credibility of these GV products, the effect of the different planes involved in the bulk adjustment method will be discussed, The gauge data will also be used to assess the impact of missing radar data (i.e., data gaps) upon the monthly radar-derived rainfall estimates. Experiments were also performed varying both the area of the radar data extracted over the gauges and the use of different weighting coefficients. Preliminary results from these sensitivity tests indicate improved agreement and consistency between the gauge and radar data.

Datta, Saswati↗

NASA NTRS1985

Estimation of precipitation from GOES IR imagery during FGGE: Application to diagnostic studies

The objectives were to (1) develop a method of estimating open-ocean rainfall and associated latent heat release via GOES IR satellite imagery; (2) to use remote precipitation estimates to investigate the role of diabatic forcing in the maintenance of the South Pacific Convergence Zone (SPCZ) during FGGE SOP-1/; and (3) to assess the significance of non-quasigeostrophic transports of energy in several cyclogenetic events preceding the development of a North Atlantic blocking episode during FGGE SOP-1. The bulk of the early FY-85 work was directed toward development of the single pixel indexing technique (SPI) which assigns a rain rate to GOES IR black-body temperatures, T sub b, via a non-linear statistical relationship developed with raingauge measurements. The method was tested against radar-derived rainfall during GATE raingauge measurements over coastal North Carolina and island stations in the South Pacific Ocean. Skill was found comparable to Arkin's method (1979 MWR) in convective situations. The results suggest that transferring a rain algorithm from one oceanic regime to another may not require substantial modification of coefficients or tunable parameters. Twelve H mean rainfall amounts were produced for the region bounded by 10 deg. N, 50 deg. S, 120 deg W and 170 deg. E during the period January 10 to 16, 1979. These estimates constitute a basic input to diagnostic calculations of diabatic heating over the SPCZ region. ECMWF level III-b data analyses was used to compute several components of the APE balance in the South Pacific during the period January 10 to 18, 1979.

Robertson, F. R.↗

NASA NTRS2011

Evaluation of Cloud-Resolving Model Intercomparison Simulations Using TWP-ICE Observations: Precipitation and Cloud Structure

The Tropical Warm Pool.International Cloud Experiment (TWP ]ICE) provided extensive observational data sets designed to initialize, force, and constrain atmospheric model simulations. In this first of a two ]part study, precipitation and cloud structures within nine cloud ]resolving model simulations are compared with scanning radar reflectivity and satellite infrared brightness temperature observations during an active monsoon period from 19 to 25 January 2006. Seven of nine simulations overestimate convective area by 20% or more leading to general overestimation of convective rainfall. This is balanced by underestimation of stratiform rainfall by 5% to 50% despite overestimation of stratiform area by up to 65% because of a preponderance of very low stratiform rain rates in all simulations. All simulations fail to reproduce observed radar reflectivity distributions above the melting level in convective regions and throughout the troposphere in stratiform regions. Observed precipitation ]sized ice reaches higher altitudes than simulated precipitation ]sized ice despite some simulations that predict lower than observed top ]of ]atmosphere infrared brightness temperatures. For the simulations that overestimate radar reflectivity aloft, graupel is the cause with one ]moment microphysics schemes whereas snow is the cause with two ]moment microphysics schemes. Differences in simulated radar reflectivity are more highly correlated with differences in mass mean melted diameter (Dm) than differences in ice water content. Dm is largely dependent on the mass ]dimension relationship and gamma size distribution parameters such as size intercept (N0) and shape parameter (m). Having variable density, variable N0, or m greater than zero produces radar reflectivities closest to those observed.

Varble, Adam↗

NASA NTRS1998

A TRMM Rainfall Estimation Method Applicable to Land Areas

Utilizing multi-spectral, dual-polarization Special Sensor Microwave Imager (SSM/I) radiometer measurements, we have developed in this study a method to retrieve average rain rate, R(sub f(sub R)), in a mesoscale grid box of 2deg x 3deg over land. The key parameter of this method is the fractional rain area, f(sub R), in that grid box, which is determined with the help of a threshold on the 85 GHz scattering depression 0 deduced from the SSM/I data. In order to demonstrate the usefulness of this method, nine-months of R(sub f(sub R))are retrieved from SSM/I data over three grid boxes in the Northeastern United States. These retrievals are then compared with the corresponding ground-truth-average rain rate, R(sub g), deduced from 15-minute rain gauges. Based on nine months of rain rate retrievals over three grid boxes, we find that R(sub f(sub R)can explain about 64 % of the variance contained in R(sub g). A similar evaluation of the grid-box-average rain rates R(sub GSCAT) and R(sub SRL), given by the NASA/GSCAT and NOAA/SRL rain retrieval algorithms, is performed. This evaluation reveals that R(sub GSCAT) and R(sub SRL) can explain only about 42 % of the variance contained in R(sub g). In our method, a threshold on the 85 GHz scattering depression is used primarily to determine the fractional rain area in a mesoscale grid box. Quantitative information pertaining to the 85 GHz scattering depression in the grid box is disregarded. In the NASA/GSCAT and NOAA/SRL methods on the other hand, this quantitative information is included. Based on the performance of all three methods, we infer that the magnitude of the scattering depression is a poor indicator of rain rate. Furthermore, from maps based on the observations made by SSM/I on land and ocean we find that there is a significant redundancy in the information content of the SSM/I multi-spectral observations. This leads us to infer that observations of SSM/I at 19 and 37 GHz add only marginal information to that given by 85 GHz scattering depression. As with other methods, the area-average rain retrieval method developed in this study needs tuning with radar and/or rain gauge observations. In the TRMM mission, the microwave radiometer rain retrieval algorithm can be tuned with TRMM radar observations. Since the radiometer has about 3.5 times wider spatial coverage compared to the radar in the TRMM mission, such an algorithm can be useful to extend geographically the rain information provided by the TRMM Precipitation Radar.

Prabhakara, C.↗

NASA NTRS2013

Application and Evaluation of a Snowmelt Runoff Model in the Tamor River Basin, Eastern Himalaya Using a Markov Chain Monte Carlo (MCMC) Data Assimilation Approach

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree-day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash-Sutcliffe metric approx. 0.84, annual volume bias <3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002-2006 period is estimated to be 29.7+/-2.9% (which includes 4.2+/-0.9% from snowfall that promptly melts), whereas 70.3+/-2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000-5500m range contributes the most to basin runoff, averaging 56.9+/-3.6% of all snowmelt input and 28.9+/-1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree-day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds.

runoff↗