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Abstract
Detailed comparisons are made between the climate simulated by a seasonal version of the NCAR Community Climate Mode) (CCM1) at 12 level, R15 spectral resolution, and that revealed by ECMWF operational analyses over 1980–86 truncated to a similar resolution. A variety of circulation statistics are presented to reveal the spatial character and seasonality of CCM1 biases in temperatures, winds, and wave flux quantities. CCM1 biases are typical of current climate models run at similar resolution. Interrelationships between the above biases are a focus of this study, in particular using wave-mean flow interaction diagnostics.
CCM1 exhibits a westerly zonal wind bias in the tropics and a lack of westerlies in the high latitude Southern Hemisphere (SH). The tropical zonal mean meridional circulation (Hadley cell) in the model is approximately a factor of two too weak. The poleward eddy heat flux is accurately simulated, but the poleward eddy momentum flux is severely underestimated, particularly in the SH. There is a resulting excessive large-scale wave drag in the model extratropical upper troposphere, in qualitative agreement with the weak model high latitude westerlies (and temperature bias patterns). Conversely, the model tropical zonal wind bias does not appear to be related to influences by large-scale waves. Wave flux biases are compared for stationary and transient statistics; model stationary waves are in good agreement with observations, while the largest relative momentum flux error is found for higher frequency transient waves.
Abstract
Detailed comparisons are made between the climate simulated by a seasonal version of the NCAR Community Climate Mode) (CCM1) at 12 level, R15 spectral resolution, and that revealed by ECMWF operational analyses over 1980–86 truncated to a similar resolution. A variety of circulation statistics are presented to reveal the spatial character and seasonality of CCM1 biases in temperatures, winds, and wave flux quantities. CCM1 biases are typical of current climate models run at similar resolution. Interrelationships between the above biases are a focus of this study, in particular using wave-mean flow interaction diagnostics.
CCM1 exhibits a westerly zonal wind bias in the tropics and a lack of westerlies in the high latitude Southern Hemisphere (SH). The tropical zonal mean meridional circulation (Hadley cell) in the model is approximately a factor of two too weak. The poleward eddy heat flux is accurately simulated, but the poleward eddy momentum flux is severely underestimated, particularly in the SH. There is a resulting excessive large-scale wave drag in the model extratropical upper troposphere, in qualitative agreement with the weak model high latitude westerlies (and temperature bias patterns). Conversely, the model tropical zonal wind bias does not appear to be related to influences by large-scale waves. Wave flux biases are compared for stationary and transient statistics; model stationary waves are in good agreement with observations, while the largest relative momentum flux error is found for higher frequency transient waves.
Abstract
The interannual variability of the top of the atmosphere net radiation budget as measured from the Nimbus- 7 Earth Radiation Budget instruments was calculated for an eight yew period 1979–1986. The largest fluctuations are shown to occur in three tropical areas. the Atlantic off the west emit of Africa, the eastern Pacific near South America, and the western Pacific northeast of Indonesia. The variability in the Atlantic was 20% greater than in the eastern Pacific and 35% greater than in the Indonesian area. The maximum anomalies in these two Pacific regions occurred during the El Niño year 1982–1983, while the maximum Atlantic anomalies. south of the Gulf of Guinea, were during 1984. An independent dataset of derived cloud type and amount from the Temperature Humidity Infrared Radiometer (THIR) and the Total Ozone Mapping Spectrometer (TOMS) instruments shows interannual changes in multilevel convective cloud systems have a minimal effect of the net balance. However changes in middle and low clouds drastically effect the balance, and are the most likely cause of the maximum radiation balance variability in the Gulf of Guinea region. This observed interannual variation of the top of the atmosphere net balance, reported in the present study, denotes the most variable “cloud radiative forcing” situation observed to date.
Abstract
The interannual variability of the top of the atmosphere net radiation budget as measured from the Nimbus- 7 Earth Radiation Budget instruments was calculated for an eight yew period 1979–1986. The largest fluctuations are shown to occur in three tropical areas. the Atlantic off the west emit of Africa, the eastern Pacific near South America, and the western Pacific northeast of Indonesia. The variability in the Atlantic was 20% greater than in the eastern Pacific and 35% greater than in the Indonesian area. The maximum anomalies in these two Pacific regions occurred during the El Niño year 1982–1983, while the maximum Atlantic anomalies. south of the Gulf of Guinea, were during 1984. An independent dataset of derived cloud type and amount from the Temperature Humidity Infrared Radiometer (THIR) and the Total Ozone Mapping Spectrometer (TOMS) instruments shows interannual changes in multilevel convective cloud systems have a minimal effect of the net balance. However changes in middle and low clouds drastically effect the balance, and are the most likely cause of the maximum radiation balance variability in the Gulf of Guinea region. This observed interannual variation of the top of the atmosphere net balance, reported in the present study, denotes the most variable “cloud radiative forcing” situation observed to date.
Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical CyclonesAbstract
The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.
Abstract
The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.
Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)Abstract
This study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.
Abstract
This study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.
Abstract
Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., −30% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%–30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.
Abstract
Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., −30% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%–30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.
A comprehensive and accurate global water vapor dataset is critical to the adequate understanding of water vapor's role in the earth's climate system. To begin to satisfy this need, the authors have produced a blended dataset made up of global, 5-yr (1988–92), l°x 1° spatial resolution, atmospheric water vapor (WV) and liquid water path products. These new products consist of both the daily total column-integrated composites and a multilayered WV product at three layers (1000–700, 700–500, 500–300 mb). The analyses combine WV retrievals from the Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the Special Sensor Microwave/Imager, and radiosonde observations. The global, vertical-layered water vapor dataset was developed by slicing the blended total column water vapor using layer information from TOVS and radiosonde. Also produced was a companion, over oceans only, liquid water path dataset. Satellite observations of liquid water path are growing in importance since many of the global climate models are now either incorporating or contain liquid water as an explicit variable. The complete dataset (all three products) has been named NVAP, an acronym for National Aeronautics and Space Administration Water Vapor Project.
This paper provides examples of the new dataset as well as scientific analysis of the observed annual cycle and the interannual variability of water vapor at global, hemispheric, and regional scales. A distinct global annual cycle is shown to be dominated by the Northern Hemisphere observations. Planetary-scale variations are found to relate well to recent independent estimates of tropospheric temperature variations. Maps of regional interannual variability in the 5-yr period show the effect of the 1992 ENSO and other features.
A comprehensive and accurate global water vapor dataset is critical to the adequate understanding of water vapor's role in the earth's climate system. To begin to satisfy this need, the authors have produced a blended dataset made up of global, 5-yr (1988–92), l°x 1° spatial resolution, atmospheric water vapor (WV) and liquid water path products. These new products consist of both the daily total column-integrated composites and a multilayered WV product at three layers (1000–700, 700–500, 500–300 mb). The analyses combine WV retrievals from the Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the Special Sensor Microwave/Imager, and radiosonde observations. The global, vertical-layered water vapor dataset was developed by slicing the blended total column water vapor using layer information from TOVS and radiosonde. Also produced was a companion, over oceans only, liquid water path dataset. Satellite observations of liquid water path are growing in importance since many of the global climate models are now either incorporating or contain liquid water as an explicit variable. The complete dataset (all three products) has been named NVAP, an acronym for National Aeronautics and Space Administration Water Vapor Project.
This paper provides examples of the new dataset as well as scientific analysis of the observed annual cycle and the interannual variability of water vapor at global, hemispheric, and regional scales. A distinct global annual cycle is shown to be dominated by the Northern Hemisphere observations. Planetary-scale variations are found to relate well to recent independent estimates of tropospheric temperature variations. Maps of regional interannual variability in the 5-yr period show the effect of the 1992 ENSO and other features.
Abstract
Refinements and improvements of an earlier technique to retrieve the cloud liquid water path (LWP) of nonprecipitating clouds over land surfaces using Special Sensor Microwave/Imager (SSM/I) 85.5-GHz measurements are presented. These techniques require estimates of the microwave surface emissivity, which are derived in clear-sky regions from SSM/I measurements and window infrared measurements from the Visible and Infrared Spin Scan Radiometer on GOES-7. A comparison of forward model calculations with SSM/I measurements in clear regions demonstrates that over a 7-day period the surface emissivities are stable.
To overcome limitations in the single-channel retrieval method under certain situations, a new method is developed that uses a normalized polarization difference (NPD) of the brightness temperatures. This method has the advantages of providing estimates of the LWP for low clouds and being extremely insensitive to the surface skin temperature. Radiative transfer simulations also show that the polarization difference at 37 GHz may be useful for retrievals in high water vapor environments and for large cloud LWP.
An intercomparison of the different retrieval methods over Platteville, Colorado, reveals large discrepancies for certain cases, but the NPD method is found to agree best with coincident ground-based microwave radiometer measurements of cloud LWP. This success is primarily due to the larger than average surface polarization differences near the Platteville site. While the NPD method shows promise in distinguishing between low, moderate, and high values of cloud LWP, a comprehensive validation effort is required to further evaluate its accuracy and limitations.
Abstract
Refinements and improvements of an earlier technique to retrieve the cloud liquid water path (LWP) of nonprecipitating clouds over land surfaces using Special Sensor Microwave/Imager (SSM/I) 85.5-GHz measurements are presented. These techniques require estimates of the microwave surface emissivity, which are derived in clear-sky regions from SSM/I measurements and window infrared measurements from the Visible and Infrared Spin Scan Radiometer on GOES-7. A comparison of forward model calculations with SSM/I measurements in clear regions demonstrates that over a 7-day period the surface emissivities are stable.
To overcome limitations in the single-channel retrieval method under certain situations, a new method is developed that uses a normalized polarization difference (NPD) of the brightness temperatures. This method has the advantages of providing estimates of the LWP for low clouds and being extremely insensitive to the surface skin temperature. Radiative transfer simulations also show that the polarization difference at 37 GHz may be useful for retrievals in high water vapor environments and for large cloud LWP.
An intercomparison of the different retrieval methods over Platteville, Colorado, reveals large discrepancies for certain cases, but the NPD method is found to agree best with coincident ground-based microwave radiometer measurements of cloud LWP. This success is primarily due to the larger than average surface polarization differences near the Platteville site. While the NPD method shows promise in distinguishing between low, moderate, and high values of cloud LWP, a comprehensive validation effort is required to further evaluate its accuracy and limitations.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
The Evolution of the Goddard Profiling Algorithm to a Fully Parametric SchemeAbstract
The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.
Abstract
The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.
