Scanning Multichannel Microwave Radiometer Data Research Articles

A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures

Abstract. The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

Trends in the sea ice cover using enhanced and compatible AMSR‐E, SSM/I, and SMMR data

Arguably, the most remarkable manifestation of change in the polar regions is the rapid decline in the Arctic perennial ice cover. Changes in the global sea ice cover, however, have been more modest, being only slightly negative in the Northern Hemisphere and even slightly positive in the Southern Hemisphere, the significance of which has not been adequately assessed because of unknown errors in the satellite historical data. Recent Advanced Microwave Scanning Radiometer (AMSR‐E) high‐resolution data are used as the baseline for generating an enhanced sea ice data set used in this study. Brightness temperature data from historical Special Scanning Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) sensors were normalized to be consistent with those from AMSR‐E, and sea ice parameters were derived from all three data sets using the same algorithm for optimum consistency and accuracy. A small bias in sea ice extent is observed between AMSR‐E and SSM/I data which, if uncorrected, causes an error of 0.62%/decade in the Arctic and 0.26%/decade in the Antarctic. Similar corrections are not needed in trend estimates of sea ice area. Biases due to seasonal changes in the accuracy of ice edge determinations, especially during melt periods, were also evaluated, and impacts on the trend results appear to be small. When updated to 2006, the trends in ice extent and area in the Arctic are now slightly more negative at −3.4 ± 0.2 and −4.0 ± 0.2% per decade, respectively, while the corresponding trends in the Antarctic remains slight but positive at 0.9 ± 0.2 and 1.7 ± 0.3% per decade.

Global Simulation of Brightness Temperatures at 6.6 and 10.7 GHz Over Land Based on SMMR Data Set Analysis

In the framework of the Soil Moisture and Ocean Salinity mission, a two-year (1987-1988) global simulation of brightness temperatures (TB) at L-band was performed using a simple model [L-band microwave emission of the biosphere, (L-MEB)] based on radiative transfer equations. However, the lack of alternative L-band spaceborne measurements corresponding to real-world data prevented from assessing the realism of the simulated global-scale TB fields. In this study, using a similar modeling approach, TB simulations were performed at C-band and X-band. These simulations required the development of C-MEB and X-MEB models, corresponding to the equivalent of L-MEB at C-band and X-band, respectively. These simulations were compared with Scanning Multichannel Microwave Radiometer (SMMR) measurements during the period January to August 1987 (corresponding to the end of life of the SMMR mission). A sensitivity study was also carried out to assess, at a global scale, the relative contributions of the main MEB parameters (particularly the roughness and vegetation model parameters). Regional differences between simulated and measured TBs were analyzed, discriminating possible issues either linked to the radiative transfer model (C-MEB and X-MEB) or due to land surface simulations. A global agreement between observations and simulations was discussed and allowed to evaluate regions where soil moisture retrievals would give best results. This comparison step made at C-band and X-band allowed to better assess how realistic and/or accurate the L-band simulations could be

WindSat radio-frequency interference signature and its identification over land and ocean

Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Microwave Scanning Radiometer-EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radiometer data, which are key information in separating RFI from natural radiations. Furthermore, it is not optimal for radiometer data observed over ocean regions due to the inherent large natural variability of spectral difference over ocean. In this paper, we first analyzed multivariate WindSat and Scanning Multichannel Microwave Radiometer (SMMR) data in terms of channel correlation, information content, and principal components of WindSat and SMMR data. Then two methods based on channel correlation were developed for RFI detection over land and ocean. Over land, we extended the spectral difference technique using principal component analysis (PCA) of RFI indices, which integrates statistics of target emission/scattering characteristics (through RFI indices) and multivariate correlation of radiometer data into a single statistical framework of PCA. Over ocean, channel regression of X-band can account for nearly all of the natural variations in the WindSat data. Therefore, we use a channel regression-based model difference technique to directly predict RFI-free brightness temperature, and therefore RFI intensity. Although model difference technique is most desirable, it is more difficult to apply over land due to heterogeneity of land surfaces. Both methods improve our knowledge of RFI signatures in terms of channel correlations and explore potential RFI mitigation, and thus provide risk reductions for future satellite passive microwave missions such as the NPOESS Conical Scanning Microwave Imager/Sounder. The new RFI algorithms are effective in detecting RFI in the C- and X-band Windsat radiometer channels over land and ocean.

Soil moisture initialization for climate prediction: Characterization of model and observation errors

While it has been shown that soil moisture data assimilation techniques can be used to constrain land surface model predictions with remotely sensed soil moisture observations to provide optimal climate model surface and root zone soil moisture initialization, a good understanding and quantification of both model and observation error are required. In this paper we therefore evaluate the catchment‐based land surface model (CLSM) and scanning multichannel microwave radiometer (SMMR) soil moisture estimation errors using long‐term in situ soil moisture measurements available for Eurasia. Generally, the CLSM surface and root zone soil moisture was found to be biased less than 0.08 vol/vol dry in dry climate and frozen soil areas and biased over 0.08 vol/vol (as high as 0.16 vol/vol) wet in wet climate areas. Moreover, the CLSM suffered from an underestimation in surface zone seasonal soil moisture variation. While the SMMR soil moisture estimates were also biased, less than 0.05 vol/vol dry in dry climate and over 0.10 vol/vol (as high as 0.2 vol/vol) wet in wet climate, they generally had accurate seasonal variations. This error characterization study is crucial for practical Eurasian data assimilation, as unbiased observations and model predictions, and reliable knowledge of relative observed and model predicted soil moisture errors are key data assimilation assumptions. This study therefore provides the error information required for data assimilation and emphasizes the need for careful bias representation when assimilating SMMR data into the CLSM. The potential deficiencies in this error assessment are acknowledged and discussed, including the disparate time‐space representation of the various soil moisture sources.

Identification of systematic bias in the cross-platform (SMMR and SSM/I) EASE-Grid brightness temperature time series

Vertically polarized 18-, 19-, and 37-GHz brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) are examined for the August 2-20, 1987 period when data from both sensors are available in the Equal-Area Scalable Earth Grid (EASE-Grid) projection. Colocated measurements over terrestrial surfaces of central North America are compared because of a previously observed inconsistency in derived winter season snow water equivalent across the cross-platform passive microwave time series. The results of this comparison show that SSM/I brightness temperatures systematically exceed SMMR measurements, with the magnitude of this difference dependant on overpass time and brightness temperature magnitude. Regression relationships are determined for adjusting EASE-Grid SMMR data to an SSM/I F8 baseline and are compared to the results of a previous study that examined daily averaged data for the polar regions. These results suggest that adjustment factors are not globally applicable; rather the region and application must be considered.

Ablation Rate Estimates over the Greenland Ice Sheet from Microwave Radiometric Data

Scanning Multichannel Microwave Radiometer (SMMR) data are used to estimate the annual melt duration (number of days with melt) for elevation transects over the Greenland ice sheet during the period from 1979-1986. The annual melt duration is used to estimate the number of positive degree days (PDDs), which are used in a degree-day mass balance model to determine ablation rates and the equilibrium line altitude (ELA). The annual melt duration along two transects estimated with SMMR data compares favorably, particularly above the ELA, to melt duration calculated from surface temperature data for the same locations. The mass balance estimates and ELA locations along eight transects agree reasonably well with measurements reported in previous studies using surface temperature data. ELAs were within 10m of published values along two transects, and the root mean square error of SMMR-derived versus surface mass balance measurements was 43mm yr−1. The estimated error in SMMR-derived ablation is between ±15% and ±50%, but could be reduced substantially by using daily microwave data available from the Special Sensor Microwave/Imager (SSM/I). This research shows the feasibility of using passive microwave data to estimate the ablation rate in order to determine ELA, which can be used to monitor the mass balance of the ice sheet.

Passive microwave data for snow-depth and snow-extent estimations in the Himalayan mountains

In high mountainous areas, such as the Himalayan Range where snow melt run-off contributes substantially to streamflow, information on snow depth and snow areal extent is vital for the estimation of streamflow and for water resources management. Microwave radiationpenetrates through clouds and snowpacks and is thus considered an important tool for providing water equivalent information on snow fields. The scanning multichannel microwave radiometer (SMMR) on board the NIMBUS-7 satellite acquired passive microwave data for 9 years (1978-1987). These SMMR data are used to test a snow algorithm that is applicable for high elevation areas (>3500m) in the Himalayan Range. The study demonstrates promising results, suggesting the application of SMMR data to derive snow-depth and snow-extent information for the Himalayan region. The generated snow maps can be used for various hydrological applications. The limited availability of field data and its comparison with the SMMR data (which are areal in nature), are major limitations in achieving close correlations between two observations. This is the first application of SMMR data for the determination of snow parameters in the Indian Himalayas. Thus, such an application sets a pace for further research and application of passive microwave data in the most rugged terrains of the world.

Analysis of merged SMMR‐SSMI time series of Arctic and Antarctic sea ice parameters 1978–1995

The most consistent means of investigating the global sea ice cover is by satellite passive microwave sensors, as these are independent of illumination and cloud cover. The Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSMI) provide information on the global sea ice cover from 1978 to present. The two instruments flew simultaneously during a 6‐week overlap period in July and August 1987, thus enabling intercomparison of the two sensors. Brightness temperatures are corrected for instrument drift and calibration differences in order to produce continuous time series of monthly averaged Arctic and Antarctic sea ice extent and sea ice area through the use of the NOrwegian Remote Sensing EXperiment (NORSEX) algorithm, which relates brightness temperatures to ice concentration. Statistical analysis on the time series estimates the decreases in Arctic ice extent and ice area to be 4.5% and 5.7%, respectively, during the 16.8‐year observation period. The overall trends established here serve to better define and strengthen earlier assertions of a reduced ice cover, based on analysis of SMMR and SSMI data taken separately. These results are consistent with GCM simulations that suggest retreat of the sea ice cover under global warming scenarios.

Results of a stratified snow emissivity model based on the wave approach: Application to the Antarctic ice sheet

The results from a snow emissivity model are used to help with the interpretation of the Scanning Multichannel Microwave Radiometer data (aboard the satellite Nimbus 7, 1978–1986) over Antarctica. The model is based on the solution of Maxwell's equations through strong fluctuation theory (Stogryn, 1986). The snow is considered isothermal with horizontal stratification, isotropic inside each stratum, and with smooth interfaces. To wipe out the interference pattern created by plane stratification and in order to take into account the spatial variability of the snow characteristics, the final result is a mean value of many computations done with many stratigraphies randomly distributed around a mean profile. The spatial distribution of the microwave signatures over Antarctica reveals three areas with typical spectral signatures. These areas are identified from the point of view of snow characteristics through a ground data set (Surdyk and FiIy, 1993). The model reproduces well the effect of the stratification on the polarization differences as observed. The strong gradient of emissivity versus frequency observed on one of the test sites is not completely understood. Depth hoar layers characterized by faceted and cup‐shaped, coarse snow grains seem to have a particular scattering behavior compared with that of the usual grains. When those coarse grains are replaced by fine grains, the model results are closer to the measured data. The presence of crusts, the roughness at the snow‐ice interfaces, and crystal orientation may also have significant effects.

An earth-gridded SSM/I data set for cryospheric studies and global change monitoring

The National Snow and Ice Data Center (NSIDC) has distributed DMSP Special Sensor Microwave Imager (SSM/I) brightness temperature grids for the Polar Regions on CD-ROM since 1987. In order to expand this product to include all potential snow covered regions, the area of coverage is now global. The format for the global SSM/I data set is the Equal Area SSM/I Earth Grid (EASE-Grid) developed at NSIDC. The EASE-Grid has been selected as the format for the NASA/NOAA Pathfinder Program Level 3 Products which include both SSM/I and SMMR (Scanning Multichannel Microwave Radiometer) data (1978–1987). Providing both data sets in the EASE-Grid will result in a 15 year time-series of satellite passive microwave data in a common format. The extent and variability of seasonal snow cover is recognized to be an important parameter in climate and hydrologic systems and trends in snow cover serve as an indicator of global climatic changes. Passive microwave data from satellites afford the possibility to monitor temporal and spatial variations in snow cover on the global scale, avoiding the problems of cloud cover and darkness. NSIDC is developing the capability to produce daily snow products from the DMSP-SSM/I satellite with a spatial resolution of 25 km. In order to provide a standard environment in which to validate SSM/I algorithm output, it is necessary to assemble baseline data sets using other, more direct, methods of measurement. NSIDC has compiled a validation data set of surface station measurements for the northern hemisphere with specific focus on the United States, Canada, and the former Soviet Union. Digital image substraction is applied to compare the surface station and satellite measurements.

Determination of inundation area in the Amazon River floodplain using the SMMR 37 GHz polarization difference

The 37 GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer (SMMR), which was operated on board the Nimbus-7 satellite, provides a sensitive indicator of surface water. These data can provide information on seasonal inundation patterns in large tropical wetlands such as the Amazon River floodplain. Although the SMMR data are of low resolution, we were able to estimate the area inundated within a group of pixels by using linear mixing models which incorporate the major end-members of the observed microwave signatures. The models were then used to estimate seasonal changes in inundation area over a 7-year period for a 34,550 km 2 area along the Amazon River near Manaus. The seasonal changes in inundation area determined using mixing models correlate well with changes in river stage.

Precipitable water over the tropical Indian Ocean derived from NIMBUS 7 satellite data ? A case study

The data from the Scanning Multichannel Microwave Radiometer (SMMR) sensor on board the NIMBUS 7 satellite in the 18 and 21 Ghz channels are used to derive the Precipitable Water (PW) over the tropical Indian Ocean for the period May–September, 1979. The PW values are derived by using both the horizontal and vertical polarization channels. These PW values along with the satellite-derived sea surface temeprature (SST) and the lower tropospheric wind (UW) data reveal the features of the summer monsoon circulation of the tropical Indian Ocean in 1979. Further, the derived PW values from Nimbus 7 SMMR data agree well with features of PW presented by Cadet (1983) inferred from the TIROS—N soundings and FGGE data.

Use of snow cover derived from satellite passive microwave data as an indicator of climate change

To assess future global change, monitoring of the climate system through observation and analysis of seasonal and interannual fluctuations of climate variables is necessary. Cryospheric elements such as snow cover are often seen as sensitive indicators and integrators of basic climate conditions and hence an indicator of regional and global change. Snow-cover elements which may serve as signals of variability and change are discussed with respect to the effective use of conventional and remotely sensed information. Conventional data are shown to be effective for assessing questions of temporal variability, but are limited for spatial variability. Passive microwave satellite data make an important contribution by providing spatial and temporal information on snow water equivalent (SWE) and the regional distribution of snowpack extent and state. Use of NIMBUS-7 SMMR (Scanning Multichannel Microwave Radiometer) data to develop a time series of SWE is assessed as a complement to conventional data. Limitations of SMMR coverage compared to DMSP SSM/I (Special Sensor Microwave/Imager) coverage for production of SWE maps for climate change analysis are discussed. Although there are limitations during early season snow cover, information derived from passive microwave data is shown to be able to map and compute the areal coverage of SWE allowing interannual comparison of the amount of water available, the date of peak accumulation and the associated spatial distribution. However, the satellite data record is still too short to establish any definitive trend in snow-cover variability.

Use of snow cover derived from satellite passive microwave data as an indicator of climate change

To assess future global change, monitoring of the climate system through observation and analysis of seasonal and interannual fluctuations of climate variables is necessary. Cryospheric elements such as snow cover are often seen as sensitive indicators and integrators of basic climate conditions and hence an indicator of regional and global change. Snow-cover elements which may serve as signals of variability and change are discussed with respect to the effective use of conventional and remotely sensed information. Conventional data are shown to be effective for assessing questions of temporal variability, but are limited for spatial variability. Passive microwave satellite data make an important contribution by providing spatial and temporal information on snow water equivalent (SWE) and the regional distribution of snowpack extent and state. Use of NIMBUS-7 SMMR (Scanning Multichannel Microwave Radiometer) data to develop a time series of SWE is assessed as a complement to conventional data. Limitations of SMMR coverage compared to DMSP SSM/I (Special Sensor Microwave/Imager) coverage for production of SWE maps for climate change analysis are discussed. Although there are limitations during early season snow cover, information derived from passive microwave data is shown to be able to map and compute the areal coverage of SWE allowing interannual comparison of the amount of water available, the date of peak accumulation and the associated spatial distribution. However, the satellite data record is still too short to establish any definitive trend in snow-cover variability.

Observing the advection of sea ice in the Weddell Sea using buoy and satellite passive microwave data

This study examines the progress and behavior of an area of sea ice as it drifts from the southwestern Weddell Sea by using data from four buoys tracked by Nimbus 6 and concurrent ice concentrations retrieved from Nimbus 7 scanning multichannel microwave radiometer data. It covers the entire growth season of 1980. The overall drift characteristics, and their relationship to ice edge displacement, are examined within the framework of four zones. Three phases are identified in the large‐scale behavior of the Weddell sea ice cover, namely, a rapid equatorward and eastward advance, a quasi‐equilibrium phase, and a period of rapid recession. A broad division is made between the sectors to the east and west of 35°–40°W in terms of the nature of ice edge advance. Transient departures from the mean northward advection are linked to the passage of cyclones; certain such events may be significant in terms of the formation and maintenance of the region of highly consolidated perennial ice observed in the western Weddell Sea. Mean daily drift rates (up to 0.9 m s−1) from within the central‐western Weddell pack are high in 1980, particularly early in the growth season and when compared with rates to the west of 50°W. Outbreaks of cold continental air alternate with incursions of relatively warm air from the north; warm conditions are recorded as far as 1200 km in from the ice edge in winter. Closed loops in the buoy trajectories, which are clockwise to the south of 63°S, reverse to become anticlockwise to the north. Mid‐ocean islands impede the eastward progress of the ice. A coherence is noted in the response of the buoys to the passage of storms, even though the buoys separated by a distance of over 100 km.

Spatial patterns of increases and decreases in the length of the sea ice season in the north polar region, 1979–1986

Recently it was reported that sea ice extents in the northern hemisphere showed a very slight but statistically significant decrease over the 8.8‐year period of the Nimbus 7 scanning multichannel microwave radiometer (SMMR) data set. In this paper the same SMMR data are used to reveal spatial patterns in increasing and decreasing sea ice coverage. Specifically, the length of the ice season is mapped for each full year of the SMMR data set (1979–1986), and the trends over the 8 years in these ice season lengths are also mapped. These trends show considerable spatial coherence, with a shortening in the sea ice season apparent in much of the eastern hemisphere of the north polar ice cover, particularly in the Sea of Okhotsk, the Barents Sea, and the Kara Sea, and a lengthening of the sea ice season apparent in much of the western hemisphere of the north polar ice cover, particularly in Davis Strait, the Labrador Sea, and the Beaufort Sea.

Linear retrieval, validation and mapping of the sea surface temperature over western mid-latitude North Pacific using Seasat SMMR data

Abstract The Scanning Multichannel Microwave Radiometer (SMMR) aboard the Seasat satellite measured emitted radiation in both horizontal and vertical polarizations at microwave frequencies of 6.6, 1069, 18.0, 21.0 and 37.0 GHz. Retrieval algorithms, for sea surface temperature (SST) determination, from subsets of one to three SMMR channels, are obtained by a two-step statistical technique. The technique first selects the best subsets of a given size defined by an R2 criterion (coefficient of determination), of a given size by the application of an efficient leaps and bounds technique on a statistical data base. It then performs a regression analysis on the selected subsets. The statistical data base employed a large (600) set of seasonally and geographically diverse atmospheric and surface parameters for radiative transfer calculations. The results of the study of one to three-channel subset retrieval algorithms indicate the possibility of using 6.6 V, 6.6 H and 18 V channels for SST determinatidn from Se...

Comparison of Nimbus-7 SMMR and GOES-1 VISSR Atmospheric Liquid Water Content

Abstract Vertically integrated atmospheric liquid water content derived from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperatures and from GOES-1 Visible and Infrared Spin-Scan Radiometer (VISSR) radiances in the visible are compared over the Indian Ocean during MONEX (monsoon experiment). In the retrieval procedure, Wilheit and Chang&apos algorithm and Stephens' parameterization schemes are applied to the SMMR and VISSR data, respectively. The results indicate that in the 0–100 mg cm−2 range of liquid water content considered, the correlation coefficient between the two types of estimates is 0.83 (0.81– 0.85 at the 99 percent confidence level). The Wilheit and Chang algorithm, however, yields values lower than those obtained with Stephens's schemes by 24.5 mg cm−2 on the average, and occasionally the SMMR-based values are negative. Alternative algorithms are proposed for use with SMMR data, which eliminate the bias, augment the correlation coefficient, and reduce the rms dif...

Liquid water content and precipitation characteristics of stratiform clouds as inferred from satellite microwave measurements

In this paper we present an analysis of the integrated liquid water content and precipitation characteristics of stratiform clouds using data from the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) for January 1979, over the North Atlantic Ocean (40°–60°N). Concurrent analysis of the SMMR data with the U.S. Air Force 3‐Dimensional Nephanalysis (3DNEPH) allows the interpretation of the SMMR‐derived liquid water paths and precipitation characteristics in terms of cloud type, cloud fraction, and cloud height. Combining the initialized analyses from the European Center for Medium‐Range Weather Forecasting (ECMWF) with the 3DNEPH enables vertical temperature and humidity profiles to be incorporated into the retrievals. The interpretation and presentation of our results are guided by their implications for the parameterization of liquid water content of layer clouds in largescale atmospheric models. The average liquid water paths for middle and low clouds were determined to be 115 and 102 g m−2, respectively, with a maximum value of 1070 g m−2. A comparison of the SMMR‐derived values of the liquid water path with the adiabatic liquid water path determined from the 3DNEPH cloud data and the ECMWF temperature profiles indicated that these clouds were for the most part substantially diluted by a combination of precipitation, freezing, and entrainment. Analysis of the liquid water path as a function of temperature showed that clouds with average temperature below 246 K had little liquid water and were inferred to be predominantly crystalline. There was little evidence that cloud liquid water path increases with temperature for cloudiness on a large scale, suggesting that cloud thickness plays the dominant role in determining cloud liquid water path. A total of 8.5% and 4.4% of the total middle and low clouds, respectively, were determined to be raining. Liquid water paths of 350 g m−2 and 500 g m−2 for middle and low clouds, respectively, were determined to be average thresholds for the onset of precipitation. Maximum rain rates for these clouds were determined to be 7 mm h−1. The autoconversion of cloud water to rain water was determined to occur at a rate of 0.001 s−1.