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Abstract
In this paper, the combined assimilation of satellite observed sea level anomalies and in situ temperature data into a global ocean model, which is used to initialize a coupled ocean–atmosphere forecast system, is described. The altimeter data are first used to create synthetic temperature observations, which are then combined with the directly observed temperature profiles in an optimum interpolation scheme. In addition to temperature, salinity is corrected based on a preservation of the model's local temperature–salinity relationship. Coupled forecasts with a lead time of up to 6 months are initialized from the ocean analyses and the impact of the data assimilation on both the ocean analysis and the coupled forecasts is investigated. It is shown that forecasts of sea surface temperature anomalies in the Niño-3 area can be improved by initializing the coupled forecast model with the ocean analysis in which temperature and altimeter data are assimilated in combination. The results further imply that a good simulation of the salinity field is required to make optimum use of the altimeter data.
Abstract
In this paper, the combined assimilation of satellite observed sea level anomalies and in situ temperature data into a global ocean model, which is used to initialize a coupled ocean–atmosphere forecast system, is described. The altimeter data are first used to create synthetic temperature observations, which are then combined with the directly observed temperature profiles in an optimum interpolation scheme. In addition to temperature, salinity is corrected based on a preservation of the model's local temperature–salinity relationship. Coupled forecasts with a lead time of up to 6 months are initialized from the ocean analyses and the impact of the data assimilation on both the ocean analysis and the coupled forecasts is investigated. It is shown that forecasts of sea surface temperature anomalies in the Niño-3 area can be improved by initializing the coupled forecast model with the ocean analysis in which temperature and altimeter data are assimilated in combination. The results further imply that a good simulation of the salinity field is required to make optimum use of the altimeter data.
Abstract
An untended instrument to measure ocean surface heat flux has been developed for use in support of field experiments and the investigation of heat flux parameterization techniques. The sensing component of the Skin-Layer Ocean Heat Flux Instrument (SOHFI) consists of two simple thermopile heat flux sensors suspended by a fiberglass mesh mounted inside a ring-shaped surface float. These sensors make direct measurements within the conduction layer, where they are held in place by a balance between surface tension and float buoyancy. The two sensors are designed with differing solar absorption properties so that surface heat flux can be distinguished from direct solar irradiance. Under laboratory conditions, the SOHFI measurements agree well with calorimetric measurements (generally to within 10%). Performance in freshwater and ocean environments is discussed in a companion paper.
Abstract
An untended instrument to measure ocean surface heat flux has been developed for use in support of field experiments and the investigation of heat flux parameterization techniques. The sensing component of the Skin-Layer Ocean Heat Flux Instrument (SOHFI) consists of two simple thermopile heat flux sensors suspended by a fiberglass mesh mounted inside a ring-shaped surface float. These sensors make direct measurements within the conduction layer, where they are held in place by a balance between surface tension and float buoyancy. The two sensors are designed with differing solar absorption properties so that surface heat flux can be distinguished from direct solar irradiance. Under laboratory conditions, the SOHFI measurements agree well with calorimetric measurements (generally to within 10%). Performance in freshwater and ocean environments is discussed in a companion paper.
Abstract
The Skin-Layer Ocean Heat Flux Instrument (SOHFI) described by Sromovsky et al. (Part I, this issue) was field-tested in a combination of freshwater and ocean deployments. Solar irradiance monitoring and field calibration techniques were demonstrated by comparison with independent measurements. Tracking of solar irradiance diurnal variations appears to be accurate to within about 5% of full scale. Preliminary field tests of the SOHFI have shown reasonably close agreement with bulk aerodynamic heat flux estimates in freshwater and ocean environments (generally within about 20%) under low to moderate wind conditions. Performance under heavy weather suggests a need to develop better methods of submergence filtering. Ocean deployments and recoveries of drifting SOHFI-equipped buoys were made during May and June 1995, during the Combined Sensor Program of 1996 in the western tropical Pacific region, and in the Greenland Sea in May 1997. The Gulf Stream and Greenland Sea deployments pointed out the need for design modifications to improve resistance to seabird attacks. Better estimates of performance and limitations of this device require extended intercomparison tests under field conditions.
Abstract
The Skin-Layer Ocean Heat Flux Instrument (SOHFI) described by Sromovsky et al. (Part I, this issue) was field-tested in a combination of freshwater and ocean deployments. Solar irradiance monitoring and field calibration techniques were demonstrated by comparison with independent measurements. Tracking of solar irradiance diurnal variations appears to be accurate to within about 5% of full scale. Preliminary field tests of the SOHFI have shown reasonably close agreement with bulk aerodynamic heat flux estimates in freshwater and ocean environments (generally within about 20%) under low to moderate wind conditions. Performance under heavy weather suggests a need to develop better methods of submergence filtering. Ocean deployments and recoveries of drifting SOHFI-equipped buoys were made during May and June 1995, during the Combined Sensor Program of 1996 in the western tropical Pacific region, and in the Greenland Sea in May 1997. The Gulf Stream and Greenland Sea deployments pointed out the need for design modifications to improve resistance to seabird attacks. Better estimates of performance and limitations of this device require extended intercomparison tests under field conditions.
Abstract
Short-range quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) are investigated for a time-lagged multimodel ensemble forecast system. One of the advantages of such an ensemble forecast system is its low-cost generation of ensemble members. In conjunction with a frequently cycling data assimilation system using a diabatic initialization [such as the Local Analysis and Prediction System (LAPS)], the time-lagged multimodel ensemble system offers a particularly appealing approach for QPF and PQPF applications. Using the NCEP stage IV precipitation analyses for verification, 6-h QPFs and PQPFs from this system are assessed during the period of March–May 2005 over the west-central United States. The ensemble system was initialized by hourly LAPS runs at a horizontal resolution of 12 km using two mesoscale models, including the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecast (WRF) model with the Advanced Research WRF (ARW) dynamic core. The 6-h PQPFs from this system provide better performance than the NCEP operational North American Mesoscale (NAM) deterministic runs at 12-km resolution, even though individual members of the MM5 or WRF models perform comparatively worse than the NAM forecasts at higher thresholds and longer lead times. Recalibration was conducted to reduce the intensity errors in time-lagged members. In spite of large biases and spatial displacement errors in the MM5 and WRF forecasts, statistical verification of QPFs and PQPFs shows more skill at longer lead times by adding more members from earlier initialized forecast cycles. Combing the two models only reduced the forecast biases. The results suggest that further studies on time-lagged multimodel ensembles for operational forecasts are needed.
Abstract
Short-range quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) are investigated for a time-lagged multimodel ensemble forecast system. One of the advantages of such an ensemble forecast system is its low-cost generation of ensemble members. In conjunction with a frequently cycling data assimilation system using a diabatic initialization [such as the Local Analysis and Prediction System (LAPS)], the time-lagged multimodel ensemble system offers a particularly appealing approach for QPF and PQPF applications. Using the NCEP stage IV precipitation analyses for verification, 6-h QPFs and PQPFs from this system are assessed during the period of March–May 2005 over the west-central United States. The ensemble system was initialized by hourly LAPS runs at a horizontal resolution of 12 km using two mesoscale models, including the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecast (WRF) model with the Advanced Research WRF (ARW) dynamic core. The 6-h PQPFs from this system provide better performance than the NCEP operational North American Mesoscale (NAM) deterministic runs at 12-km resolution, even though individual members of the MM5 or WRF models perform comparatively worse than the NAM forecasts at higher thresholds and longer lead times. Recalibration was conducted to reduce the intensity errors in time-lagged members. In spite of large biases and spatial displacement errors in the MM5 and WRF forecasts, statistical verification of QPFs and PQPFs shows more skill at longer lead times by adding more members from earlier initialized forecast cycles. Combing the two models only reduced the forecast biases. The results suggest that further studies on time-lagged multimodel ensembles for operational forecasts are needed.
Abstract
Accurate measurement of fluctuations in temperature and humidity are needed for determination of the surface evaporation rate and the air-sea sensible heat flux using either the eddy correlation or inertial dissipation method for flux calculations. These measurements are difficult to make over the ocean, and are subject to large errors when sensors are exposed to marine air containing spray droplets. All currently available commercial measurement devices for atmospheric humidity require frequent maintenance. Included in the objectives of the Humidity Exchange over the Sea program were testing and comparison of sensors used for measuring both the fluctuating and mean humidity in the marine atmosphere at high wind speeds and development of techniques for the protection of these sensors against contamination by oceanic aerosols. These sensors and droplet removal techniques are described and comparisons between measurements from several different systems are discussed in this paper.
To accomplish these goals, participating groups devised and tested three methods of removing sea spray from the sample airstream. The best performance was given by a rotating semen device, the “spray Ringer.” Several high-frequency temperature and humidity instruments, based on different physical principles, were used in the collaborative field experiment. Temperature and humidity fluctuations were measured with sufficient accuracy inside the spray removal devices using Lyman-α hygrometers and a fast thermocouple psychrometer. Comparison of several types of psychrometers (using electric thermometers) and a Rotronic MP-100 humidity sensor for measuring the mean humidity illustrated the hysteresis of the Rotronic MP-100 device after periods of high relative humidity. Confidence in the readings of the electronic psychrometer was established by in situ calibration with repeated and careful readings of ordinary hand-held Assman psychrometers (based on mercury thermometers). Electronic psychrometer employing platinum resistance thermometers perform very well.
Abstract
Accurate measurement of fluctuations in temperature and humidity are needed for determination of the surface evaporation rate and the air-sea sensible heat flux using either the eddy correlation or inertial dissipation method for flux calculations. These measurements are difficult to make over the ocean, and are subject to large errors when sensors are exposed to marine air containing spray droplets. All currently available commercial measurement devices for atmospheric humidity require frequent maintenance. Included in the objectives of the Humidity Exchange over the Sea program were testing and comparison of sensors used for measuring both the fluctuating and mean humidity in the marine atmosphere at high wind speeds and development of techniques for the protection of these sensors against contamination by oceanic aerosols. These sensors and droplet removal techniques are described and comparisons between measurements from several different systems are discussed in this paper.
To accomplish these goals, participating groups devised and tested three methods of removing sea spray from the sample airstream. The best performance was given by a rotating semen device, the “spray Ringer.” Several high-frequency temperature and humidity instruments, based on different physical principles, were used in the collaborative field experiment. Temperature and humidity fluctuations were measured with sufficient accuracy inside the spray removal devices using Lyman-α hygrometers and a fast thermocouple psychrometer. Comparison of several types of psychrometers (using electric thermometers) and a Rotronic MP-100 humidity sensor for measuring the mean humidity illustrated the hysteresis of the Rotronic MP-100 device after periods of high relative humidity. Confidence in the readings of the electronic psychrometer was established by in situ calibration with repeated and careful readings of ordinary hand-held Assman psychrometers (based on mercury thermometers). Electronic psychrometer employing platinum resistance thermometers perform very well.
Given the breadth and complexity of available data, constructing a measurement-based description of global tropospheric aerosols that will effectively confront and constrain global three-dimensional models is a daunting task. Because data are obtained from multiple sources and acquired with nonuniform spatial and temporal sampling, scales, and coverage, protocols need to be established that will organize this vast body of knowledge. Currently, there is no capability to assemble the existing aerosol data into a unified, interoperable whole. Technology advancements now being pursued in high-performance distributed computing initiatives can accomplish this objective. Once the data are organized, there are many approaches that can be brought to bear upon the problem of integrating data from different sources. These include data-driven approaches, such as geospatial statistics formulations, and model-driven approaches, such as assimilation or chemical transport modeling. Establishing a data interoperability framework will stimulate algorithm development and model validation and will facilitate the exploration of synergies between different data types. Data summarization and mining techniques can be used to make statistical inferences about climate system relationships and interpret patterns of aerosol-induced change. Generating descriptions of complex, nonlinear relationships among multiple parameters is critical to climate model improvement and validation. Finally, determining the role of aerosols in past and future climate change ultimately requires the use of fully coupled climate and chemistry models, and the evaluation of these models is required in order to trust their results. The set of recommendations presented here address one component of the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) initiative. Implementing them will produce the most accurate four-dimensional representation of global aerosols, which can then be used for testing, constraining, and validating models. These activities are critical components of a sustained program to quantify aerosol effects on global climate.
Given the breadth and complexity of available data, constructing a measurement-based description of global tropospheric aerosols that will effectively confront and constrain global three-dimensional models is a daunting task. Because data are obtained from multiple sources and acquired with nonuniform spatial and temporal sampling, scales, and coverage, protocols need to be established that will organize this vast body of knowledge. Currently, there is no capability to assemble the existing aerosol data into a unified, interoperable whole. Technology advancements now being pursued in high-performance distributed computing initiatives can accomplish this objective. Once the data are organized, there are many approaches that can be brought to bear upon the problem of integrating data from different sources. These include data-driven approaches, such as geospatial statistics formulations, and model-driven approaches, such as assimilation or chemical transport modeling. Establishing a data interoperability framework will stimulate algorithm development and model validation and will facilitate the exploration of synergies between different data types. Data summarization and mining techniques can be used to make statistical inferences about climate system relationships and interpret patterns of aerosol-induced change. Generating descriptions of complex, nonlinear relationships among multiple parameters is critical to climate model improvement and validation. Finally, determining the role of aerosols in past and future climate change ultimately requires the use of fully coupled climate and chemistry models, and the evaluation of these models is required in order to trust their results. The set of recommendations presented here address one component of the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) initiative. Implementing them will produce the most accurate four-dimensional representation of global aerosols, which can then be used for testing, constraining, and validating models. These activities are critical components of a sustained program to quantify aerosol effects on global climate.
Abstract
Descriptions of the experimental design and research highlights obtained from a series of four multiagency field projects held near Cape Canaveral, Florida, are presented. The experiments featured a 3 MW, dual-polarization, C-band Doppler radar that serves in a dual capacity as both a precipitation and cloud radar. This duality stems from a combination of the radar’s high sensitivity and extremely small-resolution volumes produced by the narrow 0.22° beamwidth and the 0.543 m along-range resolution. Experimental highlights focus on the radar’s real-time aircraft tracking capability as well as the finescale reflectivity and eddy structure of a thin nonprecipitating stratus layer. Examples of precipitating storm systems focus on the analysis of the distinctive and nearly linear radar reflectivity signatures (referred to as “streaks”) that are caused as individual hydrometeors traverse the narrow radar beam. Each streak leaves a unique radar reflectivity signature that is analyzed with regard to estimating the underlying particle properties such as size, fall speed, and oscillation characteristics. The observed along-streak reflectivity oscillations are complex and discussed in terms of diameter-dependent drop dynamics (oscillation frequency and viscous damping time scales) as well as radar-dependent factors governing the near-field Fresnel radiation pattern and inferred drop–drop interference.
Abstract
Descriptions of the experimental design and research highlights obtained from a series of four multiagency field projects held near Cape Canaveral, Florida, are presented. The experiments featured a 3 MW, dual-polarization, C-band Doppler radar that serves in a dual capacity as both a precipitation and cloud radar. This duality stems from a combination of the radar’s high sensitivity and extremely small-resolution volumes produced by the narrow 0.22° beamwidth and the 0.543 m along-range resolution. Experimental highlights focus on the radar’s real-time aircraft tracking capability as well as the finescale reflectivity and eddy structure of a thin nonprecipitating stratus layer. Examples of precipitating storm systems focus on the analysis of the distinctive and nearly linear radar reflectivity signatures (referred to as “streaks”) that are caused as individual hydrometeors traverse the narrow radar beam. Each streak leaves a unique radar reflectivity signature that is analyzed with regard to estimating the underlying particle properties such as size, fall speed, and oscillation characteristics. The observed along-streak reflectivity oscillations are complex and discussed in terms of diameter-dependent drop dynamics (oscillation frequency and viscous damping time scales) as well as radar-dependent factors governing the near-field Fresnel radiation pattern and inferred drop–drop interference.
Abstract
This paper explores the degree to which short-term forecasts with global models might be improved if clouds were fully included in a data assimilation system, so that observations of clouds affected all parts of the model state and cloud variables were adjusted during assimilation. The question is examined using a single ensemble data assimilation system coupled to two present-generation climate models with different treatments of clouds. “Perfect-model” experiments using synthetic observations, taken from a free run of the model used in subsequent assimilations, are used to circumvent complications associated with systematic model errors and observational challenges; these provide a rough upper bound on the utility of cloud observations with these models. A series of experiments is performed in which direct observations of the model’s cloud variables are added to the suite of observations being assimilated. In both models, observations of clouds reduce the 6-h forecast error, with much greater reductions in one model than in the other. Improvements are largest in regions where other observations are sparse. The two cloud schemes differ in their complexity and number of degrees of freedom; the model using the simpler scheme makes better use of the cloud observations because of the stronger correlations between cloud-related and dynamical variables (particularly temperature). This implies that the impact of real cloud observations will depend on both the strength of the instantaneous, linear relationships between clouds and other fields in the natural world, and how well each assimilating model’s cloud scheme represents those relationships.
Abstract
This paper explores the degree to which short-term forecasts with global models might be improved if clouds were fully included in a data assimilation system, so that observations of clouds affected all parts of the model state and cloud variables were adjusted during assimilation. The question is examined using a single ensemble data assimilation system coupled to two present-generation climate models with different treatments of clouds. “Perfect-model” experiments using synthetic observations, taken from a free run of the model used in subsequent assimilations, are used to circumvent complications associated with systematic model errors and observational challenges; these provide a rough upper bound on the utility of cloud observations with these models. A series of experiments is performed in which direct observations of the model’s cloud variables are added to the suite of observations being assimilated. In both models, observations of clouds reduce the 6-h forecast error, with much greater reductions in one model than in the other. Improvements are largest in regions where other observations are sparse. The two cloud schemes differ in their complexity and number of degrees of freedom; the model using the simpler scheme makes better use of the cloud observations because of the stronger correlations between cloud-related and dynamical variables (particularly temperature). This implies that the impact of real cloud observations will depend on both the strength of the instantaneous, linear relationships between clouds and other fields in the natural world, and how well each assimilating model’s cloud scheme represents those relationships.
Abstract
Many operational drought indices focus primarily on precipitation and temperature when depicting hydroclimatic anomalies, and this perspective can be augmented by analyses and products that reflect the evaporative dynamics of drought. The linkage between atmospheric evaporative demand E 0 and actual evapotranspiration (ET) is leveraged in a new drought index based solely on E 0—the Evaporative Demand Drought Index (EDDI). EDDI measures the signal of drought through the response of E 0 to surface drying anomalies that result from two distinct land surface–atmosphere interactions: 1) a complementary relationship between E 0 and ET that develops under moisture limitations at the land surface, leading to ET declining and increasing E 0, as in sustained droughts, and 2) parallel ET and E 0 increases arising from increased energy availability that lead to surface moisture limitations, as in flash droughts. To calculate EDDI from E 0, a long-term, daily reanalysis of reference ET estimated from the American Society of Civil Engineers (ASCE) standardized reference ET equation using radiation and meteorological variables from the North American Land Data Assimilation System phase 2 (NLDAS-2) is used. EDDI is obtained by deriving empirical probabilities of aggregated E 0 depths relative to their climatologic means across a user-specific time period and normalizing these probabilities. Positive EDDI values then indicate drier-than-normal conditions and the potential for drought. EDDI is a physically based, multiscalar drought index that that can serve as an indicator of both flash and sustained droughts, in some hydroclimates offering early warning relative to current operational drought indices. The performance of EDDI is assessed against other commonly used drought metrics across CONUS in .
Abstract
Many operational drought indices focus primarily on precipitation and temperature when depicting hydroclimatic anomalies, and this perspective can be augmented by analyses and products that reflect the evaporative dynamics of drought. The linkage between atmospheric evaporative demand E 0 and actual evapotranspiration (ET) is leveraged in a new drought index based solely on E 0—the Evaporative Demand Drought Index (EDDI). EDDI measures the signal of drought through the response of E 0 to surface drying anomalies that result from two distinct land surface–atmosphere interactions: 1) a complementary relationship between E 0 and ET that develops under moisture limitations at the land surface, leading to ET declining and increasing E 0, as in sustained droughts, and 2) parallel ET and E 0 increases arising from increased energy availability that lead to surface moisture limitations, as in flash droughts. To calculate EDDI from E 0, a long-term, daily reanalysis of reference ET estimated from the American Society of Civil Engineers (ASCE) standardized reference ET equation using radiation and meteorological variables from the North American Land Data Assimilation System phase 2 (NLDAS-2) is used. EDDI is obtained by deriving empirical probabilities of aggregated E 0 depths relative to their climatologic means across a user-specific time period and normalizing these probabilities. Positive EDDI values then indicate drier-than-normal conditions and the potential for drought. EDDI is a physically based, multiscalar drought index that that can serve as an indicator of both flash and sustained droughts, in some hydroclimates offering early warning relative to current operational drought indices. The performance of EDDI is assessed against other commonly used drought metrics across CONUS in .
Abstract
Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.
Abstract
Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.
