Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “rainfall rate”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2004

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

Hou, Arthur Y.↗
NASA NTRS2011

Correcting Attenuation of Reflectivity in Terminal Doppler Weather Radar

This paper documents the implementation of the algorithm for correcting attenuations in the TDWR reflectivity, and examines the impacts of performing the correction algorithm on the accuracy of the resultant precipitation products on the basis of stage IV gauge-radar analyses over six storm events (four warm season events and two cool season events). Three primary findings are summarized below: 1. Correcting for attenuation tends to improve the overall bias, the conditional bias, and the correlation between the TDWR-based rainfall estimates and stage IV values regardless of season and correction schemes. 2. Attenuation correction is overall beneficial to the accuracy of resulting rainfall estimates for the warm season. By contrast, for the cool season, despite potential improvements in the bias and correlation, attenuation correction can be undesirable when there is clear evidence of bright band enhancement arising from low freezing levels. To elaborate, in these situations spurious high precipitation rates over the melting layer can yield elevated values in specific attenuation factor K, which in turn, may lead to an artificially higher reflectivity adjustment at farther range. 3. Ingesting 3-D spatially variable RUC temperature and imposing a temperature threshold in general tend to degrade the accuracy of corrected rainfall estimates for the warm season. For the cool season, in theory they would help avoid applying correction to areas within and above the melting layer. In practice, due to the variable depth of the melting layer below the freezing level, it is difficult to determine the temperature for the lower boundary of the melting layer a priori.

Ding, Feng↗
NASA NTRS1994

Human impacts on soil carbon dynamics of deep-rooted Amazonian forests

Deforestation and logging degrade more forest in eastern and southern Amazonia than in any other region of the world. This forest alteration affects regional hydrology and the global carbon cycle, but our current understanding of these effects is limited by incomplete knowledge of tropical forest ecosystems. It is widely agreed that roots are concentrated near the soil surface in moist tropical forests, but this generalization incorrectly implies that deep roots are unimportant in water and C budgets. Our results indicate that half of the closed-canopy forests of Brazilian Amazonic occur where rainfall is highly seasonal, and these forests rely on deeply penetrating roots to extract soil water. Pasture vegetation extracts less water from deep soil than the forest it replaces, thus increasing rates of drainage and decreasing rates of evapotranspiration. Deep roots are also a source of modern carbon deep in the soil. The soils of the eastern Amazon contain more carbon below 1 m depth than is present in above-ground biomass. As much as 25 percent of this deep soil C could have annual to decadal turnover times and may be lost to the atmosphere following deforestation. We compared the importance of deep roots in a mature, evergreen forest with an adjacent man-made pasture, the most common type of vegetation on deforested land in Amazonia. The study site is near the town of Paragominas, in the Brazilian state of Para, with a seasonal rainfall pattern and deeply-weathered, kaolinitic soils that are typical for large portions of Amazonia. Root distribution, soil water extraction, and soil carbon dynamics were studied using deep auger holes and shafts in each ecosystem, and the phenology and water status of the leaf canopies were measured. We estimated the geographical distribution of deeply-rooting forests using satellite imagery, rainfall data, and field measurements.

Nepstad, Daniel C.↗
DOE OSTI2021

Above-ground net primary productivity in regenerating seasonally dry tropical forest: Contributions of rainfall, forest age and soil

Identifying factors controlling forest productivity is critical to understanding forest-climate change feedbacks, modelling vegetation dynamics and carbon finance schemes. However, little research has focused on productivity in regenerating tropical forests which are expanding in their fraction of global area have an order of magnitude larger carbon uptake rates relative to older forest. Here We examined above-ground net primary productivity (ANPP) and its components (wood production and litterfall) over 10 years in forest plots that vary in successional age, soil characteristics and species composition using band dendrometers and litterfall traps in regenerating seasonally dry tropical forests in northwestern Costa Rica. We show that the components of ANPP are differentially driven by age and annual rainfall and that local soil variation is important. Total ANPP was explained by a combination of age, annual rainfall and soil variation. Wood production comprised 35% of ANPP on average across sites and years, and was explained by annual rainfall but not forest age. Conversely, litterfall increased with forest age and soil fertility yet was not affected by annual rainfall. In this region, edaphic variability is highly correlated with plant community composition. Thus, variation in ecosystem processes explained by soil may also be partially explained by species composition. These results suggest that future changes in annual rainfall can alter the secondary forest carbon sink, but this effect will be buffered by the litterfall flux which varies little among years. In determining the long-term strength of the secondary forest carbon sink, both rainfall and forest age will be critical variables to track. We also conclude that detailed understanding of local site variation in soils and plant community may be required to accurately predict the impact of changing rainfall on forest carbon uptake. Synthesis. We show that in seasonally dry tropical forest, annual rainfall has a positive relationship with the growth of above-ground woody tissues of trees and that droughts lead to significant reductions in above-ground productivity. These results provide evidence for climate change—carbon cycle feedbacks in the seasonal tropics and highlight the value of longitudinal data on forest regeneration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data from: Temporal dynamics of free‐living nitrogen fixation in the switchgrass rhizosphere

Here we present data associated with the manuscript, Temporal dyanmics of free-living nitrogen fixation in the switchgrass rhizosphere. Free-living nitrogen fixation (FLNF) represents an important terrestrial N source and is gaining interest for its potential to contribute plant available N to bioenergy cropping systems. Switchgrass, a cellulosic bioenergy crop, may be reliant on FLNF when particularly when grown on low N marginal lands. These potential contributions of FLNF to switchgrass and the controls on this process are not well understood. We evaluated drivers of FLNF rates and N-fixing microbial community composition in field-grown switchgrass systems over two years with high temporal sampling. We found climate variables to be strong drivers of FLNF in switchgrass systems, compared to other environmental and biological factors including soil nutrients and N-fixing microbial community composition. Increased soil moisture availability tended to promote FLNF, but extreme rainfall events appeared to be detrimental. These climate-related responses suggest FLNF-derived N contributions may be reduced under projected climate shifts. We also found a significant, but weak correlation between N-fixing microbial community composition and FLNF rates including an observed shift in community composition between 2017 and 2018 and a similarly significant difference in FLNF rates between years. Lastly, we found that seasonal FLNF N contributions, based on measurement with high temporal resolution, may meet up to 80% of switchgrass N demands.

09 BIOMASS FUELS↗
NASA NTRS1992

Optimal Estimation of Rain Rate Profiles from Single-Frequency Radar Echoes

The significant ambiguities inherent in the determination of a particular vertical rain intensity profile from a given time profile of radar echo powers measured by a downward-looking (spaceborne or airborne) radar at a single attenuating frequency are well-documented. Indeed, one already knows that by appropriately varying the parameters of the reflectivity-rain-rate (Z - R) and/or attenuation-rain-rate (k - R) relationships, one can produce several substantially different hypothetical rain rate profiles which would have the same radar power profile. Imposing the additional constraint that the path-averaged rain-rate be a given fixed number does reduce the ambiguities but falls far short of eliminating them. While we now know how to generate as many mutually ambiguous rain-rate profiles from a given profile of received radar reflectivities as we like, there remains to produce a quantitative measure to assess how likely each of these profiles is, what the appropriate 'average' profile should be, and what the 'variance' of these multiple solutions is. Of course, in order to do this, one needs to spell out the stochastic constraints that can allow us to make sense of the words 'average' and 'variance' in a mathematically rigorous way. Such a quantitative approach would be particularly well-suited for such systems as the proposed Precipitation Radar of the Tropical Rainfall Measuring Mission (TRMM). Indeed, one would then be able to use the radar reflectivities measured by the TRMM radar from one particular look in order to estimate the most likely rain-rate profile that would have produced the measurements, as well as the uncertainty in the estimated rain-rates as a function of range. Such an optimal approach is described in this paper.

deep space flight computer real-time advanced spac↗
NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗
DOE OSTI2023

Simulation of compound flooding in Japan using a nationwide model

For this study, a high-resolution, summit-to-sea unstructured-grid model was used to simulate two compound flooding events in different geomorphic settings in Japan: the July 2012 flood in Kumamoto city and July 2018 flood in Okayama City, both caused by a torrential rainfall during seasonal Meiyu front but otherwise exhibiting quite different compound flood characteristics. The model shows good performance in simulating flooding extent; e.g., the Hit Rate for the inundation event in 2018 exceeds 0.9. Sensitivity tests were conducted to determine the roles/significance of levee structures along major rivers in coastal regions. Our results indicate that riverine and oceanic factors as well as levees significantly contribute to the observed flooding extents, and we are able to quantify the contributions from each factor at different locations. The model has been implemented operationally as the first nationwide 3-day compound flooding forecast system for Japan.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cloud and Precipitation Properties of MCSs Along the Meiyu Frontal Zone in Central and Southern China and Their Associated Large-Scale Environments

This study focuses on investigating the cloud and precipitation features of Meiyu mesoscale convective systems (MCSs) and their relation to the large-scale environments in central and southern China using satellite observations and reanalysis data during the period 2014–2018. MCSs from two different locations, the Yangtze River Basin (YRB) and Southern China (SC), are examined separately. The Meiyu MCSs have a mean precipitation rate of 3.6 mm/hr and contribute 20% to 60% of the total precipitation during the Meiyu period. The diurnal cycle of Meiyu MCSs shows a maximum precipitation amount in the morning, which is associated with the enhanced nocturnal low-level jet (LLJ) overnight. Although the synoptic setups in YRB and SC are found to be similar when normalized around the MCS initiation locations, MCSs exhibit some differences in terms of the cloud top height, precipitation rate, and duration, which are likely by the differences in the local forcing. Large interannual variations are found in MCSs' number, cloud size, lifetime, and rainfall intensity, which is found to be associated with the interannual variabilities in the large-scale environments. By comparing the large-scale environments with climatological mean states, we find that the year with the most intense MCS activity during the study period is characterized by an intensified southwesterly LLJ, which increases the moisture transport from the Indian Ocean and an enhancement of the midtropospheric westerly jet, which induces adiabatic ascent along the Meiyu front, creating more favorable conditions for convection.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Assessment of the quality of GATE area rainfall data from a Nimbus-5 radiometer

The quality of rainfall intensity estimates derived from passive microwave measurements by the Electrically Scanned Microwave Radiometer (ESMR-5) aboard the NIMBUS-5 satellite was evaluated. The microwave measurements used are those coincident with the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE). ESMR-5 derived rainfall intensity estimates are compared with hourly averaged GATE radar rainfall measurements. Using the radar measurements as ground truth it is determined that with the transfer curve derived herein the ratio (ESMR-5 derived rain rate)/radar measured rain rate has a mean of approximately 0.62.

Knox, N.↗
DOE OSTI2020

Distortions of the Rain Distribution With Warming, With and Without Self-Aggregation

We investigate how mesoscale circulations associated with convective aggregation can modulate the sensitivity of the hydrologic cycle to warming. We quantify changes in the full distribution of rain across radiative-convective equilibrium states in a cloud-resolving model. For a given Sea Surface Temperature (SST), the shift in mean rainfall between disorganized and organized states is associated with a shift in atmospheric radiative cooling, and is roughly analogous to the effect of a 4K SST increase. With rising temperatures, the increase in mean rain rate is insensitive to the presence of organization, while extremes can intensify faster in the aggregated state, leading to a faster amplification in the sporadic nature of rain. When convection aggregates, heavy rain is enhanced by 20%–30% and nonlinear behaviors are observed as a function of SST and strength of aggregation feedbacks. First, radiative- and surface-flux aggregation feedbacks have multiplicative effects on extremes, illustrating a non-trivial sensitivity to the degree of organization. Second, alternating Clausius-Clapeyron and super-Clausius-Clapeyron regimes in extreme rainfall are found as a function of SST, corresponding to varying thermodynamic and dynamic contributions, and a large sensitivity to precipitation efficiency variations in some SST ranges. The potential for mesoscale circulations in amplifying the hydrologic cycle is established. However, these nonlinear distortions question the quantitative relevance of idealized self-aggregation. This calls for a deeper investigation of relationships which capture the coupling between global energetics, aggregation feedbacks and local convection, and for systematic tests of their sensitivity to domain configurations, surface boundary conditions, microphysics, and turbulence schemes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Turbulent transports over tundra

An extensive period of eddy correlation surface flux measurements was conducted at a site distant from the coast on the western Alaskan tundra. The surface exchange of heat and moisture over tundra during the summer was limited by a strong resistance to transfer from the upper soil layer through the ground cover, with canopy resistances to evaporation observed to be approximately 200 s/m. Though July 1988 was anomalously warm and dry in the region and August was close to normal temperature and rainfall, there was no appreciable difference in the canopy resistance between the periods. During the dry sunny period at the end of July, the observed evaporation rate was 2 mm/d. High canopy resistance led to an approximate equipartition of net radiation between latent and sensible heat, each accounting for 40 percent of the available energy, with heat balance apparently going into soil heat flux.

Fitzjarrald, David R.↗
NASA NTRS2019

Exploiting the Convergence of Evidence in Satellite Data for Advanced Weather Index Insurance Design

The goal of drought-related weather index insurance (WII) is to protect smallholder farmers against the risk of weather shocks and to increase their agricultural productivity. Estimates of precipitation and vegetation greenness are the two dominant satellite datasets. However, ignoring additional moisture- and energy-related processes that influence the response of vegetation to rainfall leads to an incomplete representation of the hydrologic cycle. This study evaluates the added value of considering multiple independent satellite-based variables to design, calibrate, and validate weather insurance indices on the African continent. The satellite data include two rainfall datasets, soil moisture, the evaporative stress index (ESI), and vegetation greenness. We limit artificial advantages by resampling all datasets to the same spatial (0.25°) and temporal (monthly) resolution, although datasets with a higher spatial resolution might have an added value, if considered as the single source of information for localized applications. A higher correlation coefficient between the moisture-focused variables and the normalized difference vegetation index (NDVI), an indicator for vegetation vigor, provides evidence for the datasets’ capability to capture agricultural drought conditions on the ground. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall dataset, soil moisture, and ESI show higher correlations with the (lagged) NDVI in large parts of Africa, for different land covers and various climate zones, than the African Rainfall Climatology, version 2 (ARC2), rainfall dataset, which is often used in WII. A comparison to drought years as reported by farmers in Ethiopia, Senegal, and Zambia indicates a high “hit rate” of all satellite-derived anomalies regarding the detection of severe droughts but limitations regarding moderate drought events.

drought↗
NASA NTRS2000

Improving Global Reanalyses and Short Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a "1+1"D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the "1+1"D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+lD scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSM/I rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗
NASA NTRS1999

Improving Global Reanalyses and Short-Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a 1+1D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the 1+1D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSW rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Hou, Arthur Y.↗
NASA NTRS2011

The 13 years of TRMM Lightning Imaging Sensor: From Individual Flash Characteristics to Decadal Tendencies

How often lightning strikes the Earth has been the object of interest and research for decades. Several authors estimated different global flash rates using ground-based instruments, but it has been the satellite era that enabled us to monitor lightning thunderstorm activity on the time and place that lightning exactly occurs. Launched into space as a component of NASA s Tropical Rainfall Measuring Mission (TRMM) satellite, in November 1997, the Lighting Imaging Sensor (LIS) is still operating. LIS detects total lightning (i.e., intracloud and cloud-to-ground) from space in a low-earth orbit (35deg orbit). LIS has collected lightning measurements for 13 years (1998-2010) and here we present a fully revised and current total lightning climatology over the tropics. Our analysis includes the individual flash characteristics (number of events and groups, total radiance, area footprint, etc.), composite climatological maps, and trends for the observed total lightning during these 13 years. We have identified differences in the energetics of the flashes and/or the optical scattering properties of the storms cells due to cell-relative variations in microphysics and kinematics (i.e., convective or stratiform rainfall). On the climatological total lightning maps we found a dependency on the scale of analysis (resolution) in identifying the lightning maximums in the tropics. The analysis of total lightning trends observed by LIS from 1998 to 2010 in different temporal (annual and seasonal) and spatial (large and regional) scales, showed no systematic trends in the median to lower-end of the distributions, but most places in the tropics presented a decrease in the highest total lightning flash rates (higher-end of the distributions).

Albrecht, R. I.↗
NASA NTRS1993

TRMM radar

The results of a conceptual design study and the performance of key components of the Bread Board Model (BBM) of the Tropical Rainfall Measuring Mission (TRMM) radar are presented. The radar, which operates at 13.8 GHz and is designed to meet TRMM mission objectives, has a minimum measurable rain rate of 0.5 mm/h with a range resolution of 250 m, a horizontal resolution of about 4 km, and a swath width of 220 km. A 128-element active phased array system is adopted to achieve contiguous scanning within the swath. The basic characteristics of BBM were confirmed by experiments. The development of EM started with the cooperation of NASDA and CRL.

Okamoto, Kenichi↗