Abstract

Rain rate algorithms for spring, summer and fall that have been developed from comparisons between the brightness temperatures measured by the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and rain rates derived from operational WSR-57 radars over land are described. Data were utilized from a total of 25 SMMR passes and 234 radars, resulting in ∼12 000 observations of ∼1600 km² areas. Multiple correlation coefficients of 0.63, 0.80 and 0.75 are achieved for the spring, summer and fall algorithms, respectively. Most of this information is in the form of multifrequency contrast in brightness temperature, which is interpreted as a measurement of the degree to which the land-emitted radiation is attenuated by the rain systems. The SMMR 37 GH_z channel has more information on rain rate than any other channel. By combining the lower frequency channels with the 37 GH_z observations, variations in land and precipitation thermometric temperatures can be removed, leaving rain attenuation as the major effect on brightness temperature. Polarization screening at 37 GH_z is found to be sufficient to screen out cases of wet ground, which is only important when the ground is relatively vegetation free. Heavy rain cases are found to be a significant part of the algorithms' success, because of the strong microwave signatures (low brightness temperatures) that result from the presence of precipitation-sized ice in the upper portions of heavily precipitating storms. If IR data are combined with the summer microwave data, an improved (0.85) correlation with radar rain rates is achieved.

Abstract

A combination of theory and measurement is used to develop a scattering-based method for quantitatively measuring rainfall over the ocean from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) 37-GHz observations. This technique takes the observed scattering effects of precipitation on 37-GHz brightness temperatures and applies it to the oceanic environment. It requires an estimate of the effective radiating temperature of the cloudy portion of the atmosphere, and a brightness temperature measurement of the cloud-free ocean surface. These two measurements bound all possible combinations of clear and cloudy conditions within a footprint in terms of bipolarized brightness temperatures. Any satellite-observed T_B lower than these values is assumed to reflect scattering, which at 37 GHz is only due to precipitation-size hydrometeors. Because the technique involves a linear transformation between dual polarized brightness temperature and rain rate, there are no nonlinear “footprint filling” effects and a unique footprint-averaged rain rate results. It is shown that thew SMMR-derived rain rates for five cases of convection over the Gulf of Mexico are closely related to simultaneously derived radar rain rates, having a correlation of 0.90. This technique is then applied to a massive squall line over the Gulf of Mexico, and the resulting rain rate distribution reflects features found in cloud top heights and texture inferred from GOES infrared and visible imagery.

Abstract

Oceanic precipitation is estimated on a 2.5° grid for the period 1979–1991 from Microwave Sounding Unit (MSU) channels 1, 2, and 3 data gathered by seven separate TIROS-N satellites. Precipitation is diagnosed when cloud water and rainwater-induced radiometric warming of the channel 1 brightness temperatures (T _b ) exceeds a cumulative frequency distribution threshold of 15% after correction for airmass temperature determined from the channel 2 and 3 measurements. After intercalibration between satellites, the 13-year gridpoint field of average T _b warming is calibrated in precipitation units with data from five to ten years of globally distributed low-elevation island and coastal rain accumulation measurements from 132 gauges. The calibration involves a single scale factor, and has a dependence on air temperature that is estimated from an MSU climatology. Comparisons between the satellite and raingage measurements of the average annual cycle in monthly precipitation are presented for 75 raingage locations from different climatic regimes.

At 2.5° gridpoint resolution, peak annual rainfall (5600 rim) occurs in a quasistationary portion of the ITCZ over the eastern Pacific, while peak monthly rainfall (over 900 mm) occurs in the northeastern Bay of Bengal in June.

Comparisons between the MSU oceanic rainfall climatology and those of Jaeger (raingage), Legates and Wilmott (mostly ship synoptic code observations), and the GOES precipitation index (GPI) of Janowiak and Arkin (satellite infrared) reveal several important differences. Jaeger largely misses the extratropical storm tracks, as well as the intensity of the intertropical convergence zone in the eastern Pacific and western Atlantic. Legates and Wilmott have the features that Jaeger missed, but without the intensity that the MSU suggests. Two prominent differences in the Pacific ITCZ depiction are probably due to a lack of ship data in the Legates and Wilmott climatology. The GPI shows more rainfall over the eastern Indian Ocean than the MSU, and much less than the MSU over the tropical eastern Pacific, tropical western Atlantic, and in the western Pacific extratropical storm track. These differences are related to regional differences in the amount of cirrus cloud activity versus cloud water activity. The largest consistent discrepancy between the MSU and other climatologies is the eastern Pacific ITCZ, where the MSU indicates up to 8 mm day⁻¹ more rainfall than the GPI. Raingage data from the Line Islands, which protrude into one end of this maximum, suggests that the large MSU amounts could be real. Microwave sounding unit and GPI depictions of seasonal rainfall variability associated with the ENSO warm event of 1991–92 show good agreement in the large-scale patterns.

Comparisons of MSU pentad rain estimates for a small region near Sumatra to the GOES precipitation index (GPI) and two numerical weather prediction model forecasts show much better agreement between the two satellite estimates than between either satellite estimate and the model forecasts. Remaining disagreements between the two satellite methods are in the form of numerous GPI-diagnosed light-to-moderate rain amounts for which the MSU shows little or no precipitation. This is consistent with the expectation that cold cirrus cloudiness, on which the GPI estimates are based, often covers larger areas with more persistence than does the rainfall on short time scales.

Monthly gridpoint anomaly sampling skill is estimated during 81 months of two-satellite coverage by comparisons between the anomalies measured by the satellites separately. At the 2.5° gridpoint scale, anomaly correlations are generally above 0.8 in the tropics, reaching a peak of 0.99 in the center of the tropical Pacific Ocean. Extratropical gridpoint anomaly correlations are lower, due to smaller interannual variability, ranging from 0.2 to O.8. The corresponding single-satellite gridpoint anomaly sampling error ranges from 10 mm month⁻¹ to 40 mm month⁻¹, the lower errors occurring in climatologically light rainfall areas, and greater errors in the heavy rainfall areas. An example of monthly anomalies in oceanic rainfall is presented for February 1983.

Abstract

The first Advanced Microwave Sounding Unit temperature sounder (AMSU-A) was launched on the NOAA-15 satellite on 13 May 1998. The AMSU-A’s higher spatial and radiometric resolutions provide more useful information on the strength of the middle- and upper-tropospheric warm cores associated with tropical cyclones than have previous microwave temperature sounders. The gradient wind relationship suggests that the temperature gradient near the core of tropical cyclones increases nonlinearly with wind speed. The gradient wind equation is recast to include AMSU-A-derived variables. Stepwise regression is used to determine which of these variables is most closely related to maximum sustained winds (V _max). The satellite variables investigated include the radially averaged gradients at two spatial resolutions of AMSU-A channels 1–10 T _b data (δ _r T _b ), the squares of these gradients, a channel-15-based scattering index (SI₈₉), and area-averaged T _b . Calculations of T _b and δ _r T _b from mesoscale model simulations of Andrew (1992) reveal the effects of the AMSU spatial sampling on the cyclone warm core presentation. Stepwise regression of 66 AMSU-A terms against National Hurricane Center V _max estimates from the 1998 and 1999 Atlantic hurricane season confirms the existence of a nonlinear relationship between wind speed and radially averaged temperature gradients near the cyclone warm core. Of six regression terms, four are dominated by temperature information, and two are interpreted as correcting for hydrometeor contamination. Jackknifed regressions were performed to estimate the algorithm performance on independent data. For the 82 cases that had in situ measurements of V _max, the average error standard deviation was 4.7 m s⁻¹. For 108 cases without in situ wind data, the average error standard deviation was 7.5 m s⁻¹. Operational considerations, including the detection of weak cyclones and false alarm reduction, are also discussed.

Abstract

The global distribution of intense convective activity over land is shown to be measurable with satellite passive-microwave methods through a comparison of an empirical rain rate algorithm with a climatology of thunderstorm days for the months of June-August. With the 18 and 37 GHz channels of the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the strong volume scattering effects of precipitation can be measured. Even though a single frequency (37 GHz) is responsive to the scattering signature, two frequencies are needed to remove most of the effect that variations in thermometric temperatures and soil moisture have on the brightness temperatures. Because snow cover is also a volume scatterer of microwave energy at these microwavelengths, a discrimination procedure involving four of the SMMR channels is employed to separate the rain and snow classes, based upon their differences in average thermometric temperature.

Abstract

A new method for the physical retrieval of rain rates from satellite microwave radiometers is presented. The method is part of a unified ocean parameter retrieval algorithm that is based on the fundamental principles of radiative transfer. The algorithm simultaneously finds near-surface wind speed W, columnar water vapor V, columnar cloud liquid water L, rain rate R, and effective radiating temperature T _U for the upwelling radiation. Comparisons with radiosondes demonstrate that the algorithm is able to retrieve water vapor when rain is present. For rain rates from 1 to 15 mm h⁻¹, the rms difference between the retrieved water vapor and the radiosonde value is 5 mm. A novel feature of the rain retrieval method is a beamfilling correction that is based upon the ratio of the retrieved liquid water absorption coefficients at 37 and 19 GHz. This spectral ratio decreases by about 40% when heavy and light rain coexist within the SSM/I footprint as compared to the case of uniform rain. This correction increases the rain rate when the spectral ratio is small. However, even with this beamfilling correction, tropical rainfall is still unrealistically low when the freezing level in the Tropics (∼5 km) is used to specify the rain layer thickness. Realism is restored by reducing the assumed tropical rain-layer thickness to 3 km. This adjustment is probably compensating for two processes: 1) the existence of warm rain for which the rain layer does not extend to the freezing level and 2) very heavy rain for which the 19-GHz channels saturate. Global rain rates are produced for the 1991–94 period from two SSM/Is. The authors find that approximately 6% of the SSM/I observations detect measurable rain rates (R > 0.2 mm h⁻¹). The global rain maps show features that are, in general, similar to those reported in previously published rain climatologies. However, some differences that seem to be related to nonprecipitating cloud water are apparent.

Abstract

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data are used in this study as the first passive microwave information from a precessing orbit to reveal diurnal variations of precipitation over the tropical oceans (30°S–30°N). Data from three Special Sensor Microwave Imagers are combined to help alleviate the aliasing problem caused by the slow diurnal sampling of the TRMM satellite. Annual mean diurnal variations of rainfall in 1998 are presented for 10° latitude bands and six regions. The diurnal variation over all the tropical oceans exhibits an amplitude of about ±14% of the mean, and it peaks near dawn (approximately 0400–0700 LST). By latitude band, diurnal variation is most evident in the deep Tropics, while the ratio of the amplitude over the mean is relatively constant over most latitude bands. Other than in the early morning, there are no evident peaks exceeding the error bars for this analysis. By region, the coastal areas where the ITCZ intersects large continents and around the Maritime Continent are dominant. The morning preference of rainfall prevails almost everywhere in the open ocean where the mean rainfall is heavy, even though the amplitude is small compared to that near the continents.

Abstract

Po-Chedley and Fu investigated the difference in the magnitude of global temperature trends generated from the Microwave Sounding Unit (MSU) for the midtroposphere (T _MT, surface to about 75 hPa) between the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). Their approach was to examine the magnitude of a noise-reduction coefficient of one short-lived satellite, NOAA-9, which differed from UAH and RSS. Using radiosonde comparisons over a 2-yr period, they calculated an adjustment to the UAH coefficient that, when applied to the UAH data, increased the UAH global T _MT trend for 1979–2009 by +0.042 K decade⁻¹, which then happens to agree with RSS’s T _MT trend. In studying their analysis, the authors demonstrate 1) the adjustment calculated using radiosondes is inconclusive when errors are accounted for; 2) the adjustment was applied in a manner inconsistent with the UAH satellite merging strategy, creating a larger change than would be generated had the actual UAH methodology been followed; and 3) that trends of a similar product that uses the same UAH coefficient are essentially identical to UAH and RSS. Based on the authors’ previous analysis and additional work here, UAH will continue using the NOAA-9 noise-reduction coefficient, as is, for version 5.4 and the follow-on version 5.5.

Abstract

Feedbacks are widely considered to be the largest source of uncertainty in determining the sensitivity of the climate system to increasing anthropogenic greenhouse gas concentrations, yet the ability to diagnose them from observations has remained controversial. Here a simple model is used to demonstrate that any nonfeedback source of top-of-atmosphere radiative flux variations can cause temperature variability, which then results in a positive bias in diagnosed feedbacks. This effect is demonstrated with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover. The daily noise in radiative flux then causes interannual and decadal temperature variations in the model’s 50-m-deep swamp ocean. The amount of bias in the feedbacks diagnosed from time-averaged model output depends upon the size of the nonfeedback flux variability relative to the surface temperature variability, as well as the sign and magnitude of the specified (true) feedback. For model runs producing monthly shortwave flux anomaly and temperature anomaly statistics similar to those measured by satellites, the diagnosed feedbacks have positive biases generally in the range of −0.3 to −0.8 W m⁻² K⁻¹. These results suggest that current observational diagnoses of cloud feedback—and possibly other feedbacks—could be significantly biased in the positive direction.

Abstract

In Part I of this study, monthly 2.5° gridpoint anomalies in the TIROS-N satellite series Microwave Sounding Unit (MSU) channel 2 brightness temperatures during 1979–88 are evaluated with multiple satellites and radiosonde data for their climate temperature monitoring capability. The MSU anomalies are computed about a 10-year mean annual cycle at each grid point, with the MSUs intercalibrated to a common arbitrary level. The intercalibrations remove relative biases between instruments of up to several tenths of a degree celsius. The monthly gridpoint anomaly agreement between concurrently operating satellites reveals single-satellite precision generally better than 0.07°C in the tropics and better than 0.15°C at higher latitudes. Monthly anomalies in radiosonde channel 2 brightness temperatures computed with the radiative transfer equation compare very closely to the MSU measured anomalies in all climate zones, with correlations generally from 0.94 to 0.98 and standard errors of 0.15°C in the tropics to 0.30°C at high latitudes. Simplification of these radiative transfer calculations to a static weighting profile applied to the radiosonde temperature profile leads to an average degradation of only 0.02° in the monthly skill. In terms of a more traditionally measured quantity, the MSU channel 2 anomalies match best with either the radiosonde 100–20-kPa or 100–15-kPa layer anomalies. No significant spurious trends were found in the 10-yr satellite dataset compared to the radiosondes that would indicate a calibration drift in either system. Thus, sequentially launched, overlapping passive microwave radiometers provide a useful system for monitoring intraseasonal to interannual climate anomalies and offer hope for monitoring of interdecadal trends from space. The Appendix includes previously unpublished details of the MSU gridpoint anomaly dataset construction. Part II of this study addresses the removal from channel 2 of the temperature influence above the 30-kPa level, providing a sharper and thus potentially more useful weighting function for monitoring lower tropospheric temperatures.