Tropical Rainfall Mapping Mission Research Articles

A Ku-band wind and rain backscatter model at low-incidence angles using Tropical Rainfall Mapping Mission precipitation radar data

ABSTRACTThis article proposes a simple Ku-band wind and rain backscatter model at low-incidence angles for describing sea surfaces where rain is present. The data used consisted of the sea surface backscatter and averaged rain rates from Tropical Rainfall Mapping Mission precipitation radar (TRMM PR) measurements and the collocated 10 m height numerical prediction wind speeds from the European Centre for Medium-Range Weather Forecasts. From the rain effect analysis, it was found that the surface backscatter decreases with increases in the averaged rain rate. The rain-induced backscatter was clearly dependent of the wind speed and was slightly dependent of the incidence angle. On the basis of the analysis, the wind and rain backscatter model was simply defined as the sum of a wind backscatter model and a rain backscatter model. The wind backscatter model was provided with the given Ku-band low-incidence backscatter model. The rain-induced backscatter for specific incidence angles and wind speeds was modelled as a simple quadratic polynomial function of the averaged rain rate by using the collocated datasets. The wind and rain backscatter model was validated by other parts of the collocated datasets. Results showed that the new model-predicted total backscatter had a root mean square error of 1.02 dB and a correlation coefficient (r) of 0.73 with the collocated TRMM PR-measured rain-affected surface backscatter. Compared to the single wind backscatter model, the wind and rain backscatter model significantly improved the prediction accuracy for the surface backscatter in rain.

Wind speed retrieval from Ku-band Tropical Rainfall Mapping Mission precipitation radar data at low incidence angles

A Ku-band low incidence backscatter model (KuLMOD) for retrieving wind speeds from Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) data is proposed. The data set consisted of TRMM PR observations and collocated National Data Buoy Center (NDBC) and Tropical Ocean Global Atmosphere program buoy-measured wind and wave data. The TRMM PR data properties were analyzed with regard to their dependence on spatial resolution, wind speed, relative wind direction, and significant wave height. The KuLMOD model was developed using incidence angles (0.5 to 6.5 deg) and wind speeds (1.5 to 16.5 m/s) as inputs. The model coefficients were derived by fitting the collocated data. The KuLMOD-derived normalized radar cross section, σ0, was compared with those obtained from the TRMM PR observations and a quasi-specular theoretical model and showed good agreement. With the KuLMOD, the wind speeds were retrieved from the TRMM PR data using the least squares method and validated with the buoy measurements, yielding a root mean square error of 1.45 m/s. The retrieval accuracies for the different incidence angles, wind speeds, and spatial resolutions were obtained.

The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts

Abstract To effectively meet growing food demands, the global agronomic community will require a better understanding of factors that are currently limiting crop yields and where production can be viably expanded with minimal environmental consequences. Remote sensing can inform these analyses, providing valuable spatiotemporal information about yield-limiting moisture conditions and crop response under current climate conditions. In this paper we study correlations for the period 2003–2013 between yield estimates for major crops grown in Brazil and the Evaporative Stress Index (ESI) – an indicator of agricultural drought that describes anomalies in the actual/reference evapotranspiration (ET) ratio, retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The strength and timing of peak ESI-yield correlations are compared with results using remotely sensed anomalies in water supply (rainfall from the Tropical Rainfall Mapping Mission; TRMM) and biomass accumulation (LAI from the Moderate Resolution Imaging Spectroradiometer; MODIS). Correlation patterns were generally similar between all indices, both spatially and temporally, with the strongest correlations found in the south and northeast where severe flash droughts have occurred over the past decade, and where yield variability was the highest. Peak correlations tended to occur during sensitive crop growth stages. At the state scale, the ESI provided higher yield correlations for most crops and regions in comparison with TRMM and LAI anomalies. Using finer scale yield estimates reported at the municipality level, ESI correlations with soybean yields peaked higher and earlier by 10 to 25 days in comparison to TRMM and LAI, respectively. In most states, TRMM peak correlations were marginally higher on average with municipality-level annual corn yield estimates, although these estimates do not distinguish between primary and late season harvests. A notable exception occurred in the northeastern state of Bahia, where the ESI better captured effects of rapid cycling of moisture conditions on corn yields during a series of flash drought events. The results demonstrate that for monitoring agricultural drought in Brazil, value is added by combining LAI with LST indicators within a physically based model of crop water use.

An assessment of three satellite-based precipitation data sets as applied to the Thailand region

The study examines three satellite-based data sets to estimate long-term precipitation for the Thailand region: the Tropical Rainfall Mapping Mission (TRMM) 3B43, the Climate Prediction Centre morphing technique (CMORPH), and a locally developed regression model using the geostationary meteorological satellite (GMS) covering the Thailand region. Data for the first two sets were available at a spatial resolution of 0.25° × 0.25°, while the local regression model used data from the GMS at a resolution of 5 km × 5 km. The statistical regression model was developed by relating long-term monthly average precipitation from 27 rain gauge stations with concurrent satellite data in the visible and thermal infrared bands. The model was then tested against independent data from 27 rain gauge stations. Satellite/rain gauge ratios were estimated, and a smooth spline surface was used to correct the error from the model. Data from the three approaches were compared with a rain gauge network. The TRMM relation performed better than CMORPH, and the performance for GMS was comparable to TRMM with root mean square different and mean bias difference of 33.6 and 4.2%, respectively. The locally developed regression model was used to produce monthly and yearly total rainfall maps from the GMS data for the entire country.

Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003–2013

Summary Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003–2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought indicators, and used in concert may provide a more reliable evaluation of natural and managed ecosystem response to variable climate regimes.

Asymmetry and Anisotropy of Microwave Backscatter at Low Incidence Angles

This paper presents the first results from a study of upwind/downwind asymmetry (UDA) and upwind/crosswind anisotropy (UCA) of the Ku-band radar backscatter at low incidence angles (0 ° to 18°) from satellite observations. Incidence angle, wind speed, and sea state dependence were a particular focus. Data were obtained from the Ku-band HH-polarized precipitation radar data in the Tropical Rainfall Mapping Mission colocated with measurements from buoys. It is shown that there is a statistically significant directional signal in the UDA and UCA data when the radar incidence angle is greater than 5°. In particular, a higher radar backscatter was observed from the downwind as opposed to the upwind look direction. This is contrary to scatterometer measurements at moderate incidence angles. As a whole, the main trends for these negative asymmetries increase the absolute magnitudes with increasing wind speed. This UDA is explained by the use of non-Gaussian statistics of the sea surface slope. By contrast, there is a change in behavior of the UCA of microwave backscatter at a critical wind speed of about 6 to 8 m/s. The magnitude of UCA apparently decreases with increasing wind speed below this wind speed range, and then it increases for higher wind speeds. Sensitivities to both incidence angle and sea state are also analyzed.

Using MODIS Images and TRMM Data to Correlate Rainfall Peaks and Water Discharges from the Lebanese Coastal Rivers

Water flows from rivers into the sea (plumes) is a common phenomenon in many coastal zones. The hydro-logic behavior of plumes differs from one river to another depending on rainfall rate and intensity, as well as it is influenced by the hydrologic characteristics of river basin. In order to investigate the precipitation re-gime in a drainage basin versus the flow into the sea, sequential data must be available. Remotely sensed data can fulfill this scope, thus it can provide climatic and hydrologic data. The scope of this study is to monitor the behavior of water input in the catchments versus the output from rivers in the Lebanese coastal zone using remote sensing data. For this purpose, TRMM (Tropical Rainfall Mapping Mission) data and MODIS satellite images were used. Hence, rainfall data from TRMM was compared with the areal extent of water plumes from rivers. This enables establishing interpolation between water input/output for each river basin. In addition, the lag time and residence time of plumes into the sea can be measured and compared between the issuing rivers. The extracted data from remote sensing was compared with terrain measures and shows its reliability and accordance. The used approach proved to be creditable, non-invasive and cost effec-tive and can be applied to other costal river basins.

Effect of Long Waves on Ku-Band Ocean Radar Backscatter at Low Incidence Angles Using TRMM and Altimeter Data

This letter uses a large ocean satellite data set to document relationships between Ku-band radar backscatter (sigmao) of the sea surface, near-surface wind speed (U), and ocean wave height (SWH). The observations come from satellite crossovers of the Tropical Rainfall Mapping Mission (TRMM) Precipitation Radar (PR) and two satellite altimeters, namely: 1) Jason-1 and 2) ENVISAT. At these nodes, we obtain TRMM clear-air normalized radar cross-section data along with coincident altimeter-derived significant wave height. Wind speed estimates come from the European Centre for Medium-Range Weather Forecast. TRMM PR is the first satellite to measure low incidence Ku-band ocean backscatter at a continuum of incidence angles from 0deg to 18deg. This letter utilizes these global ocean data to assess hypotheses developed in past theoretical and field studies.

Absolute Calibration of Jason-1 and Envisat Altimeter Ku-Band Radar Cross Sections from Cross Comparison with TRMM Precipitation Radar Measurements

AbstractOne year of collocated, rain-free nadir Ku-band backscatter cross-section measurements from the Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) and both Jason-1 and Envisat RA-2 altimeter measurements have been compiled to compare these three sources of Ku-band radar cross section. With the exception of a +1.46 dB relative offset between Jason-1 and PR measurements and a −1.40 dB offset between Envisat and PR ones, all three Ku-band measurements compare very well in terms of dependencies upon model wind speed estimates and significant wave height measurements. The altimeter radars and the rain radar thus provide consistent measurements, and observed biases can be rationalized as differences in the radar calibration. The precipitation radar, which also covers off-nadir measurements, has been absolutely calibrated using an active radar calibrator. Consequently, the observed relative offsets can be used to indirectly calibrate both Jason-1 and Envisat altimeter Ku-band radar cross sections in an absolute sense.

Impact of rain cell on scatterometer data: 2. Correction of Seawinds measured backscatter and wind and rain flagging

Rain can strongly modify the normalized radar cross section (NRCS) measured by Ku‐band scatterometers and alter the wind vector retrieval. Part 1 of this paper (Tournadre and Quilfen, 2003) presented a theoretical model of interaction between rain and scatterometer signal and used it to quantify the effect of rain on the backscatter and on wind vectors. Their results showed that the scatterometer data are strongly affected by rain, that they are extremely sensitive to the rain distribution within scatterometer resolution cells, and that the normalized radar cross section (NRCS) variability induced by rain could be a good indicator for rain flagging. The model is further tested and validated on a tropical cyclone case using colocated high resolution rain and Seawinds NRCS data. The model is used to compute attenuation and volume scattering from Tropical Rainfall Mapping Mission Precipitation Radar (TRMM PR) rain data. The comparison of the high‐resolution (4 km) NRCS to synthetic NRCS computed from National Hurricane Center (NHC) winds and modeled rain terms shows a good qualitative agreement. The rain terms are used to correct the measured NRCS, to infer corrected winds which are significantly improved compared to NHC winds, especially for high winds. The wind correction using low‐resolution rain data (such as Special Sensor Microwave Imager (SSM/I) ones) is also investigated using rain data averaged over wind scatterometer cells. This can also significantly improve the rain retrieval. A new rain flag based on the NRCS variability within wind vector cells is presented and shown to perform better than the operational one.

Using sea surface temperature measurements from microwave and infrared satellite measurements

The Tropical Rainfall Mapping Mission Microwave Imager (TMI) instrument Sea Surface Temperature (SST) product (v1.0) is compared with in situ observations obtained in the Atlantic Ocean. The TMI SST has a mean warm bias of 0.25 K±0.7 K when compared to in situ SST at a depth of 7 m. When TMI SST are compared to in situ skin SST measurements, the bias is 0.6 K±0.5 K. A limited global comparison between TMI SST and co-incident ERS-2 Along-Track Scanning Radiometer (ATSR/2) skin SST demonstrates a bias of 0.6 K±0.6 K consistent with the result obtained using in situ observations. These results are consistent with the predicted accuracy of the TMI SST data products. Based on these results, a simple method to merge the TMI and ATSR data is proposed.

Assessment of the Jason Microwave Radiometer's Measurement of Wet Tropospheric Path Delay Using Comparisons to SSM/I and TMI

The Jason microwave radiometer (JMR) provides a crucial correction due to water vapor in the troposphere, and a much smaller correction due to liquid water, to the travel time of the Jason-1 altimeter radar pulse. An error of any size in the radiometer's measurement of wet path delay translates as an error of equal size in the measurement of sea surface height, the ultimate quantity that the altimetric system should yield. The estimate of globally-averaged sea surface height change associated with climate change, requires that uncertainties in the trends in such a global average be accurate to much better than the signal of 1–2 mm/yr. We first compare the JMR observations to those from the TOPEX/Poseidon radiometer (TMR) over approximately six months, since the intent of Jason is to continue the 10-year time series of precision ocean surface topography initiated by T/P. We then assess the stability of the JMR measurement by comparing its wet path delay to those of other orbiting radiometers over 22 months, specifically the Special Sensor Microwave Imager aboard the Defense Meteorological Satellite Program (DMSP-SSM/I) series of satellites, and the Tropical Rainfall Mapping Mission's Microwave Imager (TMI), as well as the European Center for Medium Range Weather Forecasting's (ECMWF) atmospheric numerical model estimate of water vapor. From the combined set, we obtain a robust assessment of the stability of JMR measurements. We find, that JMR is in remarkable agreement with TMR, only 2.5 mm longer, and 6–7 mm standard deviation on their difference in 0.5 degree averages; that JMR has experienced a globally-averaged step-function change, yielding an apparent shortening in wet path delay estimates of 4–5 mm around October 2002 (Jason cycles 28–32); that this step-function is visible only in the 23.8 GHz channel; and that the 34 GHz channel appears to drift at a rate of −0.4K/year. In addition, we find that, while in 2002 there was no evidence of sensitivity to the Jason satellite's attitude (a correlation of the wet path delay with yaw state), in 2003 there are strong (2–3 mm, up to 7 mm globally averaged) changes associated with such yaw state. These JMR issues were all found in the first 22 months of Jason's geophysical data records (GDR) data, and thus they apply to any investigations that use such data without further corrections.

Calibrating the quikscat/seawinds radar for measuring rainrate over the oceans

This effort continues a study of the effects of rain, over the oceans, on the signal retrieved by the SeaWinds scatterometer. It is determined that the backscatter radar cross section can be used to estimate the volumetric rain rate, averaged horizontally, across the surface resolution cells of the scatterometer. The dual polarization of the radar has a key role in developing this capability. The relative magnitudes of the radar backscatter depends on the volumetric rain rate, the rain column height and surface wind velocity, the viewing angle, as well as the polarization (due to the oblateness of raindrops at the higher rain rates). The approach to calibrating the SeaWinds normalized radar cross section (NRCS) is to collect National Weather Service Next Generation Weather Radar (NEXRAD) radar-derived rain rate measurements (4-km spatial resolution and 6-min rotating cycles) colocated in space (offshore) and time with scatterometer observations. These calibration functions lead to a Z-R relationship, which is then used at mid-ocean locations to estimate the rain rate in 0.25/spl deg/ or larger resolution cells, which are compared with Tropical Rainfall Mapping Mission (TRMM) Microwave Imager (TMI) rain estimates. Experimental results to date are in general agreement with simplified theoretical models of backscatter from rain, for this frequency, 14 GHz. These comparisons show very good agreement on a cell-by-cell basis with the TMI estimates for both wide areas (1000 km) and smaller area rain events.

Impact of rain cell on scatterometer data: 1. Theory and modeling

The two scatterometers currently in operation, the Ku‐band NASA Seawinds on the QuikScat satellite and the C‐band AMI‐Wind on the ERS‐2 satellite, are designed to infer the ocean wind vectors from sea surface radar backscatter measurements. They provide excellent coverage of the ocean, and their wind products are of great value for ocean and meteorological communities. However, the presence of rain within scatterometer cells can significantly modify the sea surface backscatter coefficient and hence alter the wind vector retrieval. These perturbations can hamper the analysis of wind fields within atmospheric low‐pressure systems or tropical cyclones. Rain perturbations result from volume scattering and attenuation by raindrops in the atmosphere as well as changes of sea surface roughness by impinging drops. For scatterometers operating at Ku‐Band, attenuation and volume scattering are strong and one order of magnitude larger than at C‐band. The wind retrieval will thus be less affected for the C‐band AMI‐Wind instrument than for the Ku‐band Seawinds. A theoretical model, based on radiative transfer formulation including rain attenuation and scattering, has been developed to quantify the modification by rain of the measured backscatter and of the retrieved wind vectors. Changes in surface roughness, a complex phenomenon not yet fully understood and parameterized, is not considered here although it could be of importance for high rain rates. As a scatterometer cell covers several hundred square kilometers, inhomogeneities of rain within the cell will further modify the measured backscatter, particularly in case of small, intense precipitating rain cells. Using analytical rain cell models and constant wind fields, the effects of partial beam filling by rain is investigated. The model results show that Ku‐band scatterometer data are greatly affected by rain and are extremely sensitive to the distribution of rain within scatterometer cells, i.e., to the distance between the rain cell center and the scatterometer resolution cell center. When the scatter from the sea surface is low, the additional volume scattering from rain will have a marked effect leading to an overestimation of the low wind speed actually present. Conversely, when the backscatter is already high (at high winds), attenuation by rain will reduce the signal causing an underestimation of the wind speed. The wind direction is modified in a complex manner and mainly depends on the rain distribution within the scatterometer cell. These results show that, especially at low and moderate wind speed, rain data such as the Special Sensor Microwave/Imager (SSM/I) rain fields are too coarse for correction of Normalized Radar Cross Section (NRCS) and that high‐resolution rain data (such as the Tropical Rainfall Mapping Mission (TRMM) ones) are necessary. They also show that a good rain flagging is still an important issue for the operational use of Ku‐band scatterometer data. A succeeding paper will present an example of application of the model for the correction of QuikScat data using TRMM rain data within a tropical cyclone.

Quantitative estimation of Tropical Rainfall Mapping Mission precipitation radar signals from ground‐based polarimetric radar observations

The Tropical Rainfall Mapping Mission (TRMM) is the first mission dedicated to measuring rainfall from space using radar. The precipitation radar (PR) is one of several instruments aboard the TRMM satellite that is operating in a nearly circular orbit with nominal altitude of 350 km, inclination of 35°, and period of 91.5 min. The PR is a single‐frequency Ku‐band instrument that is designed to yield information about the vertical storm structure so as to gain insight into the intensity and distribution of rainfall. Attenuation effects on PR measurements, however, can be significant and as high as 10–15 dB. This can seriously impair the accuracy of rain rate retrieval algorithms derived from PR signal returns. Quantitative estimation of PR attenuation is made along the PR beam via ground‐based polarimetric observations to validate attenuation correction procedures used by the PR. The reflectivity (Zh) at horizontal polarization and specific differential phase (Kdp) are found along the beam from S‐band ground radar measurements, and theoretical modeling is used to determine the expected specific attenuation (k) along the space‐Earth path at Ku‐band frequency from these measurements. A theoretical k‐Kdp relationship is determined for rain when Kdp ≥ 0.5°/km, and a power law relationship, k = a Zhb, is determined for light rain and other types of hydrometers encountered along the path. After alignment and resolution volume matching is made between ground and PR measurements, the two‐way path‐integrated attenuation (PIA) is calculated along the PR propagation path by integrating the specific attenuation along the path. The PR reflectivity derived after removing the PIA is also compared against ground radar observations.

The Relationship between Winds, Surface Roughness, and Radar Backscatter at Low Incidence Angles from TRMM Precipitation Radar Measurements

One year of collocated, rain-free normalized backscatter cross-section measurements from the Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) and wind speed estimates from the TRMM Microwave Imager are used to construct fully empirical model functions relating cross section to wind speed for incidence angles from 0° (nadir) to 18°. With the exception of a ∼1.9-dB offset, the TRMM PR model function at nadir compares well with TOPEX and Geosat results. For the first time using spaceborne data, least squares fits of the TRMM PR model functions to the geometric optics scattering formulation allow direct solution for the magnitudes and wind speed dependencies of Ku-band effective nadir reflectivity and effective mean square slope [s(u)]. Effective reflectivity is found to decrease slightly with increasing wind speed above 3.5 m s–1. A logarithmic dependence of s(u) is found for wind speeds between 1 and 10 m s–1. Both linear and two-branch logarithmic dependencies (with a slope break at 10 m s–1) are excellent and statistically indistinguishable parameterizations for s(u) for wind speeds between 5 and 19 m s–1. Calculations using the Elfouhaily et al. wave model suggest that the effective Ku-band s(u) corresponds to a cutoff wavelength of 3 to 6 times the radar wavelength for wind speeds from 5 to 25 m s–1; at lower wind speeds, the cutoff wavelength increases rapidly consistent with earlier observations.