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Abstract
A Kalman filter for the assimilation of long-lived atmospheric chemical constituents was developed for two-dimensional transport models on isentropic surfaces over the globe. Since the Kalman filter calculates the error covariances of the estimated constituent field, there are five dimensions to this problem, x 1, x 2, and time, where x 1 and x 2 are the positions of two points on an isentropic surface. Only computers with large memory capacity and high floating point speed can handle problems of this magnitude.
This article describes an implementation of the Kalman filter for distributed-memory, message-passing parallel computers. To evolve the forecast error covariance matrix, an operator decomposition and a covariance decomposition were studied. The latter was found to be scalable and has the general property, of considerable practical advantage, that the dynamical model does not need to be parallelized. Tests of the Kalman filter code examined variance transport and observability properties. This code is being used currently to assimilate constituent data retrieved by limb sounders on the Upper Atmosphere Research Satellite.
Abstract
A Kalman filter for the assimilation of long-lived atmospheric chemical constituents was developed for two-dimensional transport models on isentropic surfaces over the globe. Since the Kalman filter calculates the error covariances of the estimated constituent field, there are five dimensions to this problem, x 1, x 2, and time, where x 1 and x 2 are the positions of two points on an isentropic surface. Only computers with large memory capacity and high floating point speed can handle problems of this magnitude.
This article describes an implementation of the Kalman filter for distributed-memory, message-passing parallel computers. To evolve the forecast error covariance matrix, an operator decomposition and a covariance decomposition were studied. The latter was found to be scalable and has the general property, of considerable practical advantage, that the dynamical model does not need to be parallelized. Tests of the Kalman filter code examined variance transport and observability properties. This code is being used currently to assimilate constituent data retrieved by limb sounders on the Upper Atmosphere Research Satellite.
Abstract
A theoretical model for calculating microwave radiative transfer in raining atmospheres is developed. These calculations are compared with microwave brightness temperatures at a wavelength of 1.55 cm measured by the Electrically Scanning Microwave Radiometer (ESMR) on the Nimbus 5 satellite and rain rates derived from WSR-57 meteorological radar measurements. A specially designed ground-based verification experiment was also performed, wherein upward viewing microwave brightness temperature measurements at wavelengths of 1.55 and 0.81 cm were compared with directly measured rain rates. It is shown that over ocean areas, brightness temperature measurements from ESMR may be interpreted in terms of rain rate with about an accuracy of a factor of 2 over the range 1–25 mm h−1 rain rate.
Abstract
A theoretical model for calculating microwave radiative transfer in raining atmospheres is developed. These calculations are compared with microwave brightness temperatures at a wavelength of 1.55 cm measured by the Electrically Scanning Microwave Radiometer (ESMR) on the Nimbus 5 satellite and rain rates derived from WSR-57 meteorological radar measurements. A specially designed ground-based verification experiment was also performed, wherein upward viewing microwave brightness temperature measurements at wavelengths of 1.55 and 0.81 cm were compared with directly measured rain rates. It is shown that over ocean areas, brightness temperature measurements from ESMR may be interpreted in terms of rain rate with about an accuracy of a factor of 2 over the range 1–25 mm h−1 rain rate.
Drogue-Loss Detection for Surface Drifters during the Lagrangian Submesoscale Experiment (LASER)Abstract
The Lagrangian Submesoscale Experiment (LASER) was designed to study surface flows during winter conditions in the northern Gulf of Mexico. More than 1000 mostly biodegradable drifters were launched. The drifters consisted of a surface floater extending 5 cm below the surface, containing the satellite tracking system, and a drogue extending 60 cm below the surface, hanging beneath the floater on a flexible tether. On some floats, the drogue separated from the floater during storms. This paper describes methods to detect drogue loss based on two properties that distinguish drogued from undrogued drifters. First, undrogued drifters often flip over, pointing their satellite antenna downward and thus intermittently reducing the frequency of GPS fixes. Second, undrogued drifters respond to wind forcing more than drogued drifters. A multistage analysis is used: first, two properties are used to create a preliminary drifter classification; then, the motion of each unclassified drifter is compared to that of its classified neighbors in an iterative process for nearly all of the drifters. The algorithm classified drifters with a known drogue status with an accuracy of virtually 100%. Drogue loss times were estimated with a precision of less than 0.5 and 3 h for 60% and 85% of the drifters, respectively. An estimated 40% of the drifters lost their drogues in the first 7 weeks, with drogue loss coinciding with storm events, particularly those with steep waves. Once the drogued and undrogued drifters are classified, they can be used to quantify the differences in material dispersion at different depths.
Abstract
The Lagrangian Submesoscale Experiment (LASER) was designed to study surface flows during winter conditions in the northern Gulf of Mexico. More than 1000 mostly biodegradable drifters were launched. The drifters consisted of a surface floater extending 5 cm below the surface, containing the satellite tracking system, and a drogue extending 60 cm below the surface, hanging beneath the floater on a flexible tether. On some floats, the drogue separated from the floater during storms. This paper describes methods to detect drogue loss based on two properties that distinguish drogued from undrogued drifters. First, undrogued drifters often flip over, pointing their satellite antenna downward and thus intermittently reducing the frequency of GPS fixes. Second, undrogued drifters respond to wind forcing more than drogued drifters. A multistage analysis is used: first, two properties are used to create a preliminary drifter classification; then, the motion of each unclassified drifter is compared to that of its classified neighbors in an iterative process for nearly all of the drifters. The algorithm classified drifters with a known drogue status with an accuracy of virtually 100%. Drogue loss times were estimated with a precision of less than 0.5 and 3 h for 60% and 85% of the drifters, respectively. An estimated 40% of the drifters lost their drogues in the first 7 weeks, with drogue loss coinciding with storm events, particularly those with steep waves. Once the drogued and undrogued drifters are classified, they can be used to quantify the differences in material dispersion at different depths.
Abstract
The time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms).
Abstract
The time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms).
Abstract
Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic energy was higher over the urban domain mainly because of the shear production of building surfaces and building wakes. Direct transport of turbulent momentum flux from the LLJ to the urban street level was very small because of the relatively high elevation of the jet. However, since the LLJ dominated the mean wind in the boundary layer, the turbulence kinetic energy in the urban domain is correlated directly with the LLJ maximum speed and inversely with its height. The results indicate that the jet Richardson number is a reasonably good indicator for turbulent kinetic energy over the urban domain in the LLJ-dominated atmospheric boundary layer.
Abstract
Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic energy was higher over the urban domain mainly because of the shear production of building surfaces and building wakes. Direct transport of turbulent momentum flux from the LLJ to the urban street level was very small because of the relatively high elevation of the jet. However, since the LLJ dominated the mean wind in the boundary layer, the turbulence kinetic energy in the urban domain is correlated directly with the LLJ maximum speed and inversely with its height. The results indicate that the jet Richardson number is a reasonably good indicator for turbulent kinetic energy over the urban domain in the LLJ-dominated atmospheric boundary layer.
Abstract
High resolution, continuous current measurements made in the Korea/Tsushima Strait between May 1999 and March 2000 are used to examine current variations having time periods longer than 2 days. Twelve bottom-mounted acoustic Doppler current profilers provide velocity profiles along two sections: one section at the strait entrance southwest of Tsushima Island and the second section at the strait exit northeast of Tsushima Island. Additional measurements are provided by single moorings located between Korea and Tsushima Island and just north of Cheju Island in Cheju Strait. The two sections contain markedly different mean flow regimes. A high velocity current core exists at the southwestern section along the western slope of the strait for the entire recording period. The flow directly downstream of Tsushima Island contains large variability, and the flow is disrupted to such an extent by the island that a countercurrent commonly exists in the lee of the island. The northeastern section is marked by strong spatial variability and a large seasonal signal but in the mean consists of two localized intense flows concentrated near the Korea and Japan coasts. Peak nontidal currents exceed 70 cm s−1 while total currents exceed 120 cm s−1. The estimated mean transport calculated from the southwest line is 2.7 Sv (Sv ≡ 106 m3 s−1). EOF analyses indicate total transport variations in summer are due mainly to transport variations near the Korea coast. In winter, contributions to total transport variations are more uniformly distributed across the strait.
Abstract
High resolution, continuous current measurements made in the Korea/Tsushima Strait between May 1999 and March 2000 are used to examine current variations having time periods longer than 2 days. Twelve bottom-mounted acoustic Doppler current profilers provide velocity profiles along two sections: one section at the strait entrance southwest of Tsushima Island and the second section at the strait exit northeast of Tsushima Island. Additional measurements are provided by single moorings located between Korea and Tsushima Island and just north of Cheju Island in Cheju Strait. The two sections contain markedly different mean flow regimes. A high velocity current core exists at the southwestern section along the western slope of the strait for the entire recording period. The flow directly downstream of Tsushima Island contains large variability, and the flow is disrupted to such an extent by the island that a countercurrent commonly exists in the lee of the island. The northeastern section is marked by strong spatial variability and a large seasonal signal but in the mean consists of two localized intense flows concentrated near the Korea and Japan coasts. Peak nontidal currents exceed 70 cm s−1 while total currents exceed 120 cm s−1. The estimated mean transport calculated from the southwest line is 2.7 Sv (Sv ≡ 106 m3 s−1). EOF analyses indicate total transport variations in summer are due mainly to transport variations near the Korea coast. In winter, contributions to total transport variations are more uniformly distributed across the strait.
Abstract
Observations of rain cells in the remains of a decaying tropical storm were made by Airborne Microwave Radiometers at 19.35 and 92 GHz and three frequencies near 183 GHz. Extremely low brightness temperatures, as low as 140 K, were noted in the 92 and 183 GHz observations. These can be accounted for by the ice often associated with raindrop formation. Further, the 183 GHz observations can be interpreted in terms of the height of the ice. The brightness temperatures observed suggest the presence of precipitationsized ice as high as 9 km or more.
Abstract
Observations of rain cells in the remains of a decaying tropical storm were made by Airborne Microwave Radiometers at 19.35 and 92 GHz and three frequencies near 183 GHz. Extremely low brightness temperatures, as low as 140 K, were noted in the 92 and 183 GHz observations. These can be accounted for by the ice often associated with raindrop formation. Further, the 183 GHz observations can be interpreted in terms of the height of the ice. The brightness temperatures observed suggest the presence of precipitationsized ice as high as 9 km or more.
Dynamical Seasonal Prediction (DSP) is an informally coordinated multi-institution research project to investigate the predictability of seasonal mean atmospheric circulation and rainfall. The basic idea is to test the feasibility of extending the technology of routine numerical weather prediction beyond the inherent limit of deterministic predictability of weather to produce numerical climate predictions using state-of-the-art global atmospheric models. Atmospheric general circulation models (AGCMs) either forced by predicted sea surface temperature (SST) or as part of a coupled forecast system have shown in the past that certain regions of the extratropics, in particular, the Pacific–North America (PNA) region during Northern Hemisphere winter, can be predicted with significant skill especially during years of large tropical SST anomalies. However, there is still a great deal of uncertainty about how much the details of various AGCMs impact conclusions about extratropical seasonal prediction and predictability.
DSP is designed to compare seasonal simulation and prediction results from five state-of-the-art U.S. modeling groups (NCAR, COLA, GSFC, GFDL, NCEP) in order to assess which aspects of the results are robust and which are model dependent. The initial emphasis is on the predictability of seasonal anomalies over the PNA region. This paper also includes results from the ECMWF model, and historical forecast skill over both the PNA region and the European region is presented for all six models.
It is found that with specified SST boundary conditions, all models show that the winter season mean circulation anomalies over the Pacific–North American region are highly predictable during years of large tropical sea surface temperature anomalies. The influence of large anomalous boundary conditions is so strong and so reproducible that the seasonal mean forecasts can be given with a high degree of confidence. However, the degree of reproducibility is highly variable from one model to the other, and quantities such as the PNA region signal to noise ratio are found to vary significantly between the different AGCMs. It would not be possible to make reliable estimates of predictability of the seasonal mean atmosphere circulation unless causes for such large differences among models are understood.
Dynamical Seasonal Prediction (DSP) is an informally coordinated multi-institution research project to investigate the predictability of seasonal mean atmospheric circulation and rainfall. The basic idea is to test the feasibility of extending the technology of routine numerical weather prediction beyond the inherent limit of deterministic predictability of weather to produce numerical climate predictions using state-of-the-art global atmospheric models. Atmospheric general circulation models (AGCMs) either forced by predicted sea surface temperature (SST) or as part of a coupled forecast system have shown in the past that certain regions of the extratropics, in particular, the Pacific–North America (PNA) region during Northern Hemisphere winter, can be predicted with significant skill especially during years of large tropical SST anomalies. However, there is still a great deal of uncertainty about how much the details of various AGCMs impact conclusions about extratropical seasonal prediction and predictability.
DSP is designed to compare seasonal simulation and prediction results from five state-of-the-art U.S. modeling groups (NCAR, COLA, GSFC, GFDL, NCEP) in order to assess which aspects of the results are robust and which are model dependent. The initial emphasis is on the predictability of seasonal anomalies over the PNA region. This paper also includes results from the ECMWF model, and historical forecast skill over both the PNA region and the European region is presented for all six models.
It is found that with specified SST boundary conditions, all models show that the winter season mean circulation anomalies over the Pacific–North American region are highly predictable during years of large tropical sea surface temperature anomalies. The influence of large anomalous boundary conditions is so strong and so reproducible that the seasonal mean forecasts can be given with a high degree of confidence. However, the degree of reproducibility is highly variable from one model to the other, and quantities such as the PNA region signal to noise ratio are found to vary significantly between the different AGCMs. It would not be possible to make reliable estimates of predictability of the seasonal mean atmosphere circulation unless causes for such large differences among models are understood.
Abstract
We examine thermodynamic and kinematic structures of terrain trapped airflows (TTAs) during an atmospheric river (AR) event impacting Northern California 10–11 March 2016 using Alpha Jet Atmospheric eXperiment (AJAX) aircraft data, in situ observations, and Weather and Research Forecasting (WRF) Model simulations. TTAs are identified by locally intensified low-level winds flowing parallel to the coastal ranges and having maxima over the near-coastal waters. Multiple mechanisms can produce TTAs, including terrain blocking and gap flows. The changes in winds can significantly alter the distribution, timing, and intensity of precipitation. We show here how different mechanisms producing TTAs evolve during this event and influence local precipitation variations. Three different periods are identified from the time-varying wind fields. During period 1 (P1), a TTA develops during synoptic-scale onshore flow that backs to southerly flow near the coast. This TTA occurs when the Froude number (Fr) is less than 1, suggesting low-level terrain blocking is the primary mechanism. During period 2 (P2), a Petaluma offshore gap flow develops, with flows turning parallel to the coast offshore and with Fr > 1. Periods P1 and P2 are associated with slightly more coastal than mountain precipitation. In period 3 (P3), the gap flow initiated during P2 merges with a pre-cold-frontal low-level jet (LLJ) and enhanced precipitation shifts to higher mountain regions. Dynamical mixing also becomes more important as the TTA becomes confluent with the approaching LLJ. The different mechanisms producing TTAs and their effects on precipitation pose challenges to observational and modeling systems needed to improve forecasts and early warnings of AR events.
Abstract
We examine thermodynamic and kinematic structures of terrain trapped airflows (TTAs) during an atmospheric river (AR) event impacting Northern California 10–11 March 2016 using Alpha Jet Atmospheric eXperiment (AJAX) aircraft data, in situ observations, and Weather and Research Forecasting (WRF) Model simulations. TTAs are identified by locally intensified low-level winds flowing parallel to the coastal ranges and having maxima over the near-coastal waters. Multiple mechanisms can produce TTAs, including terrain blocking and gap flows. The changes in winds can significantly alter the distribution, timing, and intensity of precipitation. We show here how different mechanisms producing TTAs evolve during this event and influence local precipitation variations. Three different periods are identified from the time-varying wind fields. During period 1 (P1), a TTA develops during synoptic-scale onshore flow that backs to southerly flow near the coast. This TTA occurs when the Froude number (Fr) is less than 1, suggesting low-level terrain blocking is the primary mechanism. During period 2 (P2), a Petaluma offshore gap flow develops, with flows turning parallel to the coast offshore and with Fr > 1. Periods P1 and P2 are associated with slightly more coastal than mountain precipitation. In period 3 (P3), the gap flow initiated during P2 merges with a pre-cold-frontal low-level jet (LLJ) and enhanced precipitation shifts to higher mountain regions. Dynamical mixing also becomes more important as the TTA becomes confluent with the approaching LLJ. The different mechanisms producing TTAs and their effects on precipitation pose challenges to observational and modeling systems needed to improve forecasts and early warnings of AR events.
Abstract
We present an analysis of ocean surface dispersion characteristics, on 1–100-m scales, obtained by optically tracking a release of
Abstract
We present an analysis of ocean surface dispersion characteristics, on 1–100-m scales, obtained by optically tracking a release of
