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Systematic Differences in Aircraft and Radiosonde Temperatures
Implications for NWP and Climate Studies
Automated aircraft data are very important as input to numerical weather prediction (NWP) models because of their accuracy, large quantity, and extensive and different data coverage compared to radiosonde data. On average, aircraft mean temperature observation increments [MTOI; defined here as the observations minus the corresponding 6-h forecast (background)] are more positive (warmer) than radiosondes, especially around jet level. Temperatures from different model types of aircraft exhibit a large variance in MTOI that vary with both pressure and the phase of flight (POF), confirmed by collocation studies. This paper compares temperatures of aircraft and radiosondes by collocation and MTOI differences, along with discussing the pros and cons of each method, with neither providing an absolute truth.
Arguments are presented for estimating bias corrections of aircraft temperatures before input into NWP models based on the difference of their MTOI and that of radiosondes, which tends to cancel systematic errors in the background while using the radiosondes as truth. These corrections are just estimates because radiosonde temperatures have uncertainty and the NCEP background has systematic errors, in particular an MTOI of almost 2°C at the tropopause that is attributable in part to vertical interpolation errors, which can be reduced by increasing model vertical resolution. The estimated temperature bias corrections are predominantly negative, of the order of 0.5°–1.0°C, with relatively small monthly changes, and often have vertically deep amplitudes.
This study raises important issues pertaining to the NWP, aviation, and climate communities. Further metadata from the aviation community, field experiments comparing temperature measurements, and input from other NWP centers are recommended for refining bias corrections.
Automated aircraft data are very important as input to numerical weather prediction (NWP) models because of their accuracy, large quantity, and extensive and different data coverage compared to radiosonde data. On average, aircraft mean temperature observation increments [MTOI; defined here as the observations minus the corresponding 6-h forecast (background)] are more positive (warmer) than radiosondes, especially around jet level. Temperatures from different model types of aircraft exhibit a large variance in MTOI that vary with both pressure and the phase of flight (POF), confirmed by collocation studies. This paper compares temperatures of aircraft and radiosondes by collocation and MTOI differences, along with discussing the pros and cons of each method, with neither providing an absolute truth.
Arguments are presented for estimating bias corrections of aircraft temperatures before input into NWP models based on the difference of their MTOI and that of radiosondes, which tends to cancel systematic errors in the background while using the radiosondes as truth. These corrections are just estimates because radiosonde temperatures have uncertainty and the NCEP background has systematic errors, in particular an MTOI of almost 2°C at the tropopause that is attributable in part to vertical interpolation errors, which can be reduced by increasing model vertical resolution. The estimated temperature bias corrections are predominantly negative, of the order of 0.5°–1.0°C, with relatively small monthly changes, and often have vertically deep amplitudes.
This study raises important issues pertaining to the NWP, aviation, and climate communities. Further metadata from the aviation community, field experiments comparing temperature measurements, and input from other NWP centers are recommended for refining bias corrections.
Abstract
The entrainment of clear air and its subsequent mixing with a filament of cloudy air, as occurs at the edge of a cloud, is studied in three-dimensional direct numerical simulations that combine the Eulerian description of the turbulent velocity, temperature, and vapor fields with a Lagrangian cloud droplet ensemble. Forced and decaying turbulence is considered, such as when the dynamics around the filament is driven by larger-scale eddies or during the final period of the life cycle of a cloud. The microphysical response depicted in n d − 〈r 3〉 space (where n d and r are droplet number density and radius, respectively) shows characteristics of both homogeneous and inhomogeneous mixing, depending on the Damköhler number. The transition from inhomogeneous to homogeneous mixing leads to an offset of the homogeneous mixing curve to larger dilution fractions. The response of the system is governed by the smaller of the single droplet evaporation time scale and the bulk phase relaxation time scale. Variability within the n d − 〈r 3〉 space increases with decreasing sample volume, especially during the mixing transients. All of these factors have implications for the interpretation of measurements in clouds. The qualitative mixing behavior changes for forced versus decaying turbulence, with the latter yielding remnant patches of unmixed cloud and stronger fluctuations. Buoyancy due to droplet evaporation is observed to play a minor role in the mixing for the present configuration. Finally, the mixing process leads to the transient formation of a pronounced nearly exponential tail of the probability density function of the Lagrangian supersaturation, and a similar tail emerges in the droplet size distribution under inhomogeneous conditions.
Abstract
The entrainment of clear air and its subsequent mixing with a filament of cloudy air, as occurs at the edge of a cloud, is studied in three-dimensional direct numerical simulations that combine the Eulerian description of the turbulent velocity, temperature, and vapor fields with a Lagrangian cloud droplet ensemble. Forced and decaying turbulence is considered, such as when the dynamics around the filament is driven by larger-scale eddies or during the final period of the life cycle of a cloud. The microphysical response depicted in n d − 〈r 3〉 space (where n d and r are droplet number density and radius, respectively) shows characteristics of both homogeneous and inhomogeneous mixing, depending on the Damköhler number. The transition from inhomogeneous to homogeneous mixing leads to an offset of the homogeneous mixing curve to larger dilution fractions. The response of the system is governed by the smaller of the single droplet evaporation time scale and the bulk phase relaxation time scale. Variability within the n d − 〈r 3〉 space increases with decreasing sample volume, especially during the mixing transients. All of these factors have implications for the interpretation of measurements in clouds. The qualitative mixing behavior changes for forced versus decaying turbulence, with the latter yielding remnant patches of unmixed cloud and stronger fluctuations. Buoyancy due to droplet evaporation is observed to play a minor role in the mixing for the present configuration. Finally, the mixing process leads to the transient formation of a pronounced nearly exponential tail of the probability density function of the Lagrangian supersaturation, and a similar tail emerges in the droplet size distribution under inhomogeneous conditions.
Abstract
A three-step approach to develop a framework for dynamical seasonal prediction of precipitation over the U.S. Affiliated Pacific Islands (USAPI) is adopted. First, guided by the climatological features of basic variables, a view that climates of the USAPI are connected by large-scale phenomena involving the warm pool, South Pacific convergence zone, tropical monsoons, and subtropical anticyclone is proposed. Second, prediction skill in ensemble hindcasts performed with the Climate Forecast System, version 2 (CFSv2), is evaluated with the hypothesis that ENSO is the leading candidate for large and persisting precipitation departures. Third, moist static energy budget diagnostics are performed to identify physical processes responsible for precipitation anomalies.
At leads of 0–6 months, CFSv2 demonstrates useful skill in predicting Niño-3.4 SST and equatorial Pacific precipitation anomalies. During El Niño, positive precipitation anomalies along the central (eastern) equatorial Pacific are anchored by net radiative flux (F rad) and moist advection (evaporation and F rad). The model’s skill in predicting precipitation anomalies over South Pacific (Hawaiian) islands is highest (lowest). Over the west Pacific islands, the skill is low during the rainy season. During El Niño, skill over the USAPI, in particular predicting dryness persistence at long leads is useful. Suppressed precipitation over the Hawaiian and South Pacific (west Pacific) islands are determined by anomalous dry and cold air advection (reduced evaporation and F rad). These processes are local, but are dictated by circulation anomalies forced by ENSO. Model budget estimates are qualitatively consistent with those obtained from reanalysis, boosting confidence for societal benefits. However, observational constraints, as well as budget residuals, pose limitations.
Abstract
A three-step approach to develop a framework for dynamical seasonal prediction of precipitation over the U.S. Affiliated Pacific Islands (USAPI) is adopted. First, guided by the climatological features of basic variables, a view that climates of the USAPI are connected by large-scale phenomena involving the warm pool, South Pacific convergence zone, tropical monsoons, and subtropical anticyclone is proposed. Second, prediction skill in ensemble hindcasts performed with the Climate Forecast System, version 2 (CFSv2), is evaluated with the hypothesis that ENSO is the leading candidate for large and persisting precipitation departures. Third, moist static energy budget diagnostics are performed to identify physical processes responsible for precipitation anomalies.
At leads of 0–6 months, CFSv2 demonstrates useful skill in predicting Niño-3.4 SST and equatorial Pacific precipitation anomalies. During El Niño, positive precipitation anomalies along the central (eastern) equatorial Pacific are anchored by net radiative flux (F rad) and moist advection (evaporation and F rad). The model’s skill in predicting precipitation anomalies over South Pacific (Hawaiian) islands is highest (lowest). Over the west Pacific islands, the skill is low during the rainy season. During El Niño, skill over the USAPI, in particular predicting dryness persistence at long leads is useful. Suppressed precipitation over the Hawaiian and South Pacific (west Pacific) islands are determined by anomalous dry and cold air advection (reduced evaporation and F rad). These processes are local, but are dictated by circulation anomalies forced by ENSO. Model budget estimates are qualitatively consistent with those obtained from reanalysis, boosting confidence for societal benefits. However, observational constraints, as well as budget residuals, pose limitations.
Abstract
Detailed correlation analysis of the all-India monsoon rainfall and mean sea-level seasonal pressure at Bombay (19°N, 73°E) up to three lags on either side of the monsoon wren during the last 30 years (1951–80) indicates a systematic relationship. The winter-to-premonsoon (March, April, May–Deceinber, January, February; MAM–DJF) seasonal pressure tendency at Bombay shows a correlation coefficient (CC) of −0.70 (significant at 0.1% level) with the Indian monsoon rainfall.
Further examination of this relationship over a long period of 144 years (1847–1990), using sliding correlation analysis, reveals some interesting features. The sliding CCs were positive before 1870, negative during 1871–1900, positive in the years 1901–40, and again negative later on, showing systematic turning points around the years 1870, 1900, and 1940. In light of other corroborative evidence, these climatic regimes can be identified as “meridional monsoon” periods during 1871–1900 and after 1940, and as “zonal monsoon” periods before 1870 and during 1901–40, similar to the observation of Fu and Fletcher. It is also observed that the relationship between Bombay pressure and Indian monsoon rainfall becomes dominant when the ENSO variance in Bombay pressure is high and falls apart when the ENSO variance is small.
The paper contains a listing of the long homogeneous data series on all-India monsoon rainfall and monthly MSL pressure at Bombay for the period 1847–1990.
Abstract
Detailed correlation analysis of the all-India monsoon rainfall and mean sea-level seasonal pressure at Bombay (19°N, 73°E) up to three lags on either side of the monsoon wren during the last 30 years (1951–80) indicates a systematic relationship. The winter-to-premonsoon (March, April, May–Deceinber, January, February; MAM–DJF) seasonal pressure tendency at Bombay shows a correlation coefficient (CC) of −0.70 (significant at 0.1% level) with the Indian monsoon rainfall.
Further examination of this relationship over a long period of 144 years (1847–1990), using sliding correlation analysis, reveals some interesting features. The sliding CCs were positive before 1870, negative during 1871–1900, positive in the years 1901–40, and again negative later on, showing systematic turning points around the years 1870, 1900, and 1940. In light of other corroborative evidence, these climatic regimes can be identified as “meridional monsoon” periods during 1871–1900 and after 1940, and as “zonal monsoon” periods before 1870 and during 1901–40, similar to the observation of Fu and Fletcher. It is also observed that the relationship between Bombay pressure and Indian monsoon rainfall becomes dominant when the ENSO variance in Bombay pressure is high and falls apart when the ENSO variance is small.
The paper contains a listing of the long homogeneous data series on all-India monsoon rainfall and monthly MSL pressure at Bombay for the period 1847–1990.
Abstract
This study investigates the predictability of seasonal mean circulation anomalies associated purely with the influence of anomalous sea surface temperatures (SSTs). Within this framework, seasonal mean atmospheric anomalies on a case by case basis are understood to consist of a potentially predictable boundary-forced component and an unpredictable naturally varying component. The predictive capability of an atmospheric general circulation model (AGCM) for seasonal timescales should therefore be assessed in terms of the average skill over many cases, since it is only then that the boundary-forced predictable signal in observations can be identified.
To illustrate, experiments for 1982–1993 using two versions of an AGCM are presented. The models, referred to here as MRF8 and MRF9, differ in the parameterization of a single process. Each model is run nine times for the 12 years using different initial conditions but identical observed global SSTS. The nine-member ensemble mean anomalies for each season in 1982–1993 are compared with observed anomalies over the Pacific–North American (PNA) region.
Several different measures of the impact of SST boundary forcing on the extratropical flow suggest that MRF9 is a better model for seasonal prediction purposes. The two AGCMs have substantially different zonal-mean climatologies in the Tropics and subtropics, with MRF9 significantly better. It is argued that the improved mean flow in MRF9 enhances its midlatitude sensitivity to tropical forcing. The results highlight the importance of continued GCM development and give reason to hope that an even better model would lead to further improved forecasts of seasonal anomalies over the PNA sector.
Abstract
This study investigates the predictability of seasonal mean circulation anomalies associated purely with the influence of anomalous sea surface temperatures (SSTs). Within this framework, seasonal mean atmospheric anomalies on a case by case basis are understood to consist of a potentially predictable boundary-forced component and an unpredictable naturally varying component. The predictive capability of an atmospheric general circulation model (AGCM) for seasonal timescales should therefore be assessed in terms of the average skill over many cases, since it is only then that the boundary-forced predictable signal in observations can be identified.
To illustrate, experiments for 1982–1993 using two versions of an AGCM are presented. The models, referred to here as MRF8 and MRF9, differ in the parameterization of a single process. Each model is run nine times for the 12 years using different initial conditions but identical observed global SSTS. The nine-member ensemble mean anomalies for each season in 1982–1993 are compared with observed anomalies over the Pacific–North American (PNA) region.
Several different measures of the impact of SST boundary forcing on the extratropical flow suggest that MRF9 is a better model for seasonal prediction purposes. The two AGCMs have substantially different zonal-mean climatologies in the Tropics and subtropics, with MRF9 significantly better. It is argued that the improved mean flow in MRF9 enhances its midlatitude sensitivity to tropical forcing. The results highlight the importance of continued GCM development and give reason to hope that an even better model would lead to further improved forecasts of seasonal anomalies over the PNA sector.
Abstract
Conditions producing disastrous flooding in Uttarakhand, India, in June 2013 differed from conditions that produced other notorious floods in the Himalayan region in recent years. During the week preceding the Uttarakhand flood, deep convection moistened the mountainsides, making them vulnerable to flooding. However, the precipitation producing the flood was not associated with a deep convective event. Rather, an eastward-propagating upper-level trough in the westerlies extended abnormally far southward, with the jet reaching the Himalayas. The south end of the trough merged with a monsoon low moving westward across India. The merged system produced persistent moist low-level flow oriented normal to the Himalayas that advected large amounts of water vapor into the Uttarakhand region. The flow was moist neutral when it passed over the Himalayan barrier, and orographic lifting produced heavy continuous rain over the region for 2–3 days. The precipitation was largely stratiform in nature although embedded convection of moderate depth occurred along the foothills, where some mild instability was being released. The Uttarakhand flood had characteristics in common with major 2013 floods in the Rocky Mountains in Colorado and Alberta, Canada.
Abstract
Conditions producing disastrous flooding in Uttarakhand, India, in June 2013 differed from conditions that produced other notorious floods in the Himalayan region in recent years. During the week preceding the Uttarakhand flood, deep convection moistened the mountainsides, making them vulnerable to flooding. However, the precipitation producing the flood was not associated with a deep convective event. Rather, an eastward-propagating upper-level trough in the westerlies extended abnormally far southward, with the jet reaching the Himalayas. The south end of the trough merged with a monsoon low moving westward across India. The merged system produced persistent moist low-level flow oriented normal to the Himalayas that advected large amounts of water vapor into the Uttarakhand region. The flow was moist neutral when it passed over the Himalayan barrier, and orographic lifting produced heavy continuous rain over the region for 2–3 days. The precipitation was largely stratiform in nature although embedded convection of moderate depth occurred along the foothills, where some mild instability was being released. The Uttarakhand flood had characteristics in common with major 2013 floods in the Rocky Mountains in Colorado and Alberta, Canada.
Abstract
In this paper, statistical properties of rainfall are derived from 14 years of Tropical Rainfall Measuring Mission data to optimize the use of flight hours for the upcoming High Altitude Ice Crystals (HAIC)/High Ice Water Content (HIWC) program. This program aims to investigate the convective processes responsible for the generation of the high ice water content that has been recognized as a threat to civil aviation. The probability that convective cells are conducive to HIWC is also further investigated using three years of C-band polarimetric radar data. Further insights into the variability of convective rainfall and favorable conditions for HIWC are also gained using two different methods to characterize the large-scale atmospheric conditions around Darwin, Australia (the Madden–Julian oscillation and the Darwin atmospheric regimes), and the underlying surface type (oceanic vs continental). The main results from the climatology relevant to flight-plan decision making are (i) convective cells conducive to HIWC should be found close to Darwin, (ii) at least 90% of convective cells are conducive to HIWC at 10- and 12-km flight levels, (iii) multiple flights per day in favorable large-scale conditions will be needed so as to utilize the 150 project flight hours, (iv) the largest numbers of HIWC radar pixels are found around 0300 and 1500 local time, and (v) to fulfill the requirement to fly 90 h in oceanic convection and 60 h in or around continental convection, a minimum “acceptable” size of the convective area has been derived and should serve as a guideline for flight-decision purposes.
Abstract
In this paper, statistical properties of rainfall are derived from 14 years of Tropical Rainfall Measuring Mission data to optimize the use of flight hours for the upcoming High Altitude Ice Crystals (HAIC)/High Ice Water Content (HIWC) program. This program aims to investigate the convective processes responsible for the generation of the high ice water content that has been recognized as a threat to civil aviation. The probability that convective cells are conducive to HIWC is also further investigated using three years of C-band polarimetric radar data. Further insights into the variability of convective rainfall and favorable conditions for HIWC are also gained using two different methods to characterize the large-scale atmospheric conditions around Darwin, Australia (the Madden–Julian oscillation and the Darwin atmospheric regimes), and the underlying surface type (oceanic vs continental). The main results from the climatology relevant to flight-plan decision making are (i) convective cells conducive to HIWC should be found close to Darwin, (ii) at least 90% of convective cells are conducive to HIWC at 10- and 12-km flight levels, (iii) multiple flights per day in favorable large-scale conditions will be needed so as to utilize the 150 project flight hours, (iv) the largest numbers of HIWC radar pixels are found around 0300 and 1500 local time, and (v) to fulfill the requirement to fly 90 h in oceanic convection and 60 h in or around continental convection, a minimum “acceptable” size of the convective area has been derived and should serve as a guideline for flight-decision purposes.
Abstract
Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93.
The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST.
Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.
Abstract
Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93.
The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST.
Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.
