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Abstract
The sensitivity of tropical atmospheric hydrologic processes to cloud microphysics is investigated using the NASA Goddard Earth Observing System (GEOS) general circulation model (GCM). Results show that a faster autoconversion rate leads to (a) enhanced deep convection in the climatological convective zones anchored to tropical land regions; (b) more warm rain, but less cloud over oceanic regions; and (c) an increased convective-to-stratiform rain ratio over the entire Tropics. Fewer clouds enhance longwave cooling and reduce shortwave heating in the upper troposphere, while more warm rain produces more condensation heating in the lower troposphere. This vertical differential heating destabilizes the tropical atmosphere, producing a positive feedback resulting in more rain and an enhanced atmospheric water cycle over the Tropics. The feedback is maintained via secondary circulations between convective tower and anvil regions (cold rain), and adjacent middle-to-low cloud (warm rain) regions. The lower cell is capped by horizontal divergence and maximum cloud detrainment near the freezing–melting (0°C) level, with rising motion (relative to the vertical mean) in the warm rain region connected to sinking motion in the cold rain region. The upper cell is found above the 0°C level, with induced subsidence in the warm rain and dry regions, coupled to forced ascent in the deep convection region.
It is that warm rain plays an important role in regulating the time scales of convective cycles, and in altering the tropical large-scale circulation through radiative–dynamic interactions. Reduced cloud–radiation feedback due to a faster autoconversion rate results in intermittent but more energetic eastward propagating Madden–Julian oscillations (MJOs). Conversely, a slower autoconversion rate, with increased cloud radiation produces MJOs with more realistic westward-propagating transients embedded in eastward-propagating supercloud clusters. The implications of the present results on climate change and water cycle dynamics research are discussed.
Abstract
The sensitivity of tropical atmospheric hydrologic processes to cloud microphysics is investigated using the NASA Goddard Earth Observing System (GEOS) general circulation model (GCM). Results show that a faster autoconversion rate leads to (a) enhanced deep convection in the climatological convective zones anchored to tropical land regions; (b) more warm rain, but less cloud over oceanic regions; and (c) an increased convective-to-stratiform rain ratio over the entire Tropics. Fewer clouds enhance longwave cooling and reduce shortwave heating in the upper troposphere, while more warm rain produces more condensation heating in the lower troposphere. This vertical differential heating destabilizes the tropical atmosphere, producing a positive feedback resulting in more rain and an enhanced atmospheric water cycle over the Tropics. The feedback is maintained via secondary circulations between convective tower and anvil regions (cold rain), and adjacent middle-to-low cloud (warm rain) regions. The lower cell is capped by horizontal divergence and maximum cloud detrainment near the freezing–melting (0°C) level, with rising motion (relative to the vertical mean) in the warm rain region connected to sinking motion in the cold rain region. The upper cell is found above the 0°C level, with induced subsidence in the warm rain and dry regions, coupled to forced ascent in the deep convection region.
It is that warm rain plays an important role in regulating the time scales of convective cycles, and in altering the tropical large-scale circulation through radiative–dynamic interactions. Reduced cloud–radiation feedback due to a faster autoconversion rate results in intermittent but more energetic eastward propagating Madden–Julian oscillations (MJOs). Conversely, a slower autoconversion rate, with increased cloud radiation produces MJOs with more realistic westward-propagating transients embedded in eastward-propagating supercloud clusters. The implications of the present results on climate change and water cycle dynamics research are discussed.
Abstract
Two 3-year (1979–1982) integrations were carried out with a version of the GLA GCM that contains the Simple Biosphere Model (SiB) for simulating land-atmosphere interactions. The control case used the usual SiB vegetation cover (comprising 12 vegetation types), while its twin, the deforestation case, imposed a scenario in which all tropical rainforests were entirely replaced by grassland. Except for this difference, all other initial and prescribed boundary conditions were kept identical in both integrations.
An intercomparison of the integrations shows that tropical deforestation
• decreases evapotranspiration and increases land surface outgoing longwave radiation and sensible heat flux, thereby warming and drying the planetary boundary layer. This happens despite the reduced absorption of solar radiation due to higher surface albedo of the deforested land.
• produces significant and robust local as well as global climate changes. The local effect includes significant changes (mostly reductions) in precipitation and diabatic heating, while the large-scale effect is to weaken the Hadley circulation but invigorate the southern Ferrel cell, drawing larger air mass from the indirect polar cells.
• decreases the surface stress (drag force) owing to reduced surface roughness of deforested land, which in turn intensifies winds in the planetary boundary layer, thereby affecting the dynamic structure of moisture convergence. The simulated surface winds are about 70% stronger and are accompanied by significant changes in the power spectrum of the annual cycle of surface and PBL winds and precipitation.
• Our results broadly confirm several findings of recent tropical deforestation simulation experiments. In addition, some global-scale climatic influences of deforestation not identified in earlier studies are delineated.
Abstract
Two 3-year (1979–1982) integrations were carried out with a version of the GLA GCM that contains the Simple Biosphere Model (SiB) for simulating land-atmosphere interactions. The control case used the usual SiB vegetation cover (comprising 12 vegetation types), while its twin, the deforestation case, imposed a scenario in which all tropical rainforests were entirely replaced by grassland. Except for this difference, all other initial and prescribed boundary conditions were kept identical in both integrations.
An intercomparison of the integrations shows that tropical deforestation
• decreases evapotranspiration and increases land surface outgoing longwave radiation and sensible heat flux, thereby warming and drying the planetary boundary layer. This happens despite the reduced absorption of solar radiation due to higher surface albedo of the deforested land.
• produces significant and robust local as well as global climate changes. The local effect includes significant changes (mostly reductions) in precipitation and diabatic heating, while the large-scale effect is to weaken the Hadley circulation but invigorate the southern Ferrel cell, drawing larger air mass from the indirect polar cells.
• decreases the surface stress (drag force) owing to reduced surface roughness of deforested land, which in turn intensifies winds in the planetary boundary layer, thereby affecting the dynamic structure of moisture convergence. The simulated surface winds are about 70% stronger and are accompanied by significant changes in the power spectrum of the annual cycle of surface and PBL winds and precipitation.
• Our results broadly confirm several findings of recent tropical deforestation simulation experiments. In addition, some global-scale climatic influences of deforestation not identified in earlier studies are delineated.
Abstract
The homogeneous nucleation theory of liquid droplets in supersaturated vapors is reviewed. Taking into consideration the microscopic surface tension and extrapolating from the triple point to the critical point (T = Te ) of the liquid-gas phase transition, we reexamine homogeneous nucleation theory. A calculation of the growth rate for microscopic clusters due to the incorporation of much smaller clusters (instead of single molecules) is given and an appropriate variable, scaled supersaturation, is presented to study the mechanism of homogeneous nucleation. For a given nucleation rate, the scaled supersaturation is expected to be nearly independent of temperature below Te .; this is confirmed by experimental data. A generalized form for the droplet model is proposed. Previous theories (“classical,” Lothe-Pound, Reiss, Katz, Cohen) are shown to be special cases of this generalized form and all are shown to be invalid near the critical point. A quantitative theory is made by extrapolating Fisher's droplet model from the critical region to the triple point. For the free energy of embryo formation we include the contributions due to internal vibrations and self-avoiding walks. The microscopic surface tension for the droplet is estimated from the measured coexistence curve; it is found to agree with the bulk macroscopic surface tension near the triple and critical points and to be smaller in the intermediate region. With these considerations we have calculated the scaled supersaturation for water vapor as a function of temperature for given measured nucleation rates. The proposed theory is in good agreement with experiment.
Abstract
The homogeneous nucleation theory of liquid droplets in supersaturated vapors is reviewed. Taking into consideration the microscopic surface tension and extrapolating from the triple point to the critical point (T = Te ) of the liquid-gas phase transition, we reexamine homogeneous nucleation theory. A calculation of the growth rate for microscopic clusters due to the incorporation of much smaller clusters (instead of single molecules) is given and an appropriate variable, scaled supersaturation, is presented to study the mechanism of homogeneous nucleation. For a given nucleation rate, the scaled supersaturation is expected to be nearly independent of temperature below Te .; this is confirmed by experimental data. A generalized form for the droplet model is proposed. Previous theories (“classical,” Lothe-Pound, Reiss, Katz, Cohen) are shown to be special cases of this generalized form and all are shown to be invalid near the critical point. A quantitative theory is made by extrapolating Fisher's droplet model from the critical region to the triple point. For the free energy of embryo formation we include the contributions due to internal vibrations and self-avoiding walks. The microscopic surface tension for the droplet is estimated from the measured coexistence curve; it is found to agree with the bulk macroscopic surface tension near the triple and critical points and to be smaller in the intermediate region. With these considerations we have calculated the scaled supersaturation for water vapor as a function of temperature for given measured nucleation rates. The proposed theory is in good agreement with experiment.
Abstract
The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.
Abstract
The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.
Abstract
Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.
Abstract
Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.
Abstract
The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.
Abstract
The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.
Abstract
Testbeds have become integral to advancing the transfer of knowledge and capabilities from research to operational weather forecasting in many parts of the world. The first high-impact weather testbed in tropical Africa was recently carried out through the African Science for Weather Information and Forecasting Techniques (SWIFT) program, with participation from researchers and forecasters from Senegal, Ghana, Nigeria, Kenya, the United Kingdom, and international and pan-African organizations. The testbed aims were to trial new forecasting and nowcasting products with operational forecasters, to inform future research, and to act as a template for future testbeds in the tropics. The African SWIFT testbed integrated users and researchers throughout the process to facilitate development of impact-based forecasting methods and new research ideas driven both by operations and user input. The new products are primarily satellite-based nowcasting systems and ensemble forecasts at global and regional convection-permitting scales. Neither of these was used operationally in the participating African countries prior to the testbed. The testbed received constructive, positive feedback via intense user interaction including fishery, agriculture, aviation, and electricity sectors. After the testbed, a final set of recommended standard operating procedures for satellite-based nowcasting in tropical Africa have been produced. The testbed brought the attention of funding agencies and organizational directors to the immediate benefit of improved forecasts. Delivering the testbed strengthened the partnership between each country’s participating university and weather forecasting agency and internationally, which is key to ensuring the longevity of the testbed outcomes.
Abstract
Testbeds have become integral to advancing the transfer of knowledge and capabilities from research to operational weather forecasting in many parts of the world. The first high-impact weather testbed in tropical Africa was recently carried out through the African Science for Weather Information and Forecasting Techniques (SWIFT) program, with participation from researchers and forecasters from Senegal, Ghana, Nigeria, Kenya, the United Kingdom, and international and pan-African organizations. The testbed aims were to trial new forecasting and nowcasting products with operational forecasters, to inform future research, and to act as a template for future testbeds in the tropics. The African SWIFT testbed integrated users and researchers throughout the process to facilitate development of impact-based forecasting methods and new research ideas driven both by operations and user input. The new products are primarily satellite-based nowcasting systems and ensemble forecasts at global and regional convection-permitting scales. Neither of these was used operationally in the participating African countries prior to the testbed. The testbed received constructive, positive feedback via intense user interaction including fishery, agriculture, aviation, and electricity sectors. After the testbed, a final set of recommended standard operating procedures for satellite-based nowcasting in tropical Africa have been produced. The testbed brought the attention of funding agencies and organizational directors to the immediate benefit of improved forecasts. Delivering the testbed strengthened the partnership between each country’s participating university and weather forecasting agency and internationally, which is key to ensuring the longevity of the testbed outcomes.
