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- Author or Editor: T. S. Kang x
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Abstract
During the Joint Tropical Rain Experiment of the Malaysian Meteorological Service and the University of Toronto, pulsating raindrop ensembles, hereafter pulses, were observed in and around Penang Island. Using a Doppler radar on 25 October 1990, a periodic variation of precipitation aloft 30 km from the radar site, with an approximate 8-min period, was established and seemed to be caused by the evolution and motion of horizontal inhomogeneities existing within the same cell. On 30 October 1990, using a new volume scanning strategy with a repetition cycle of 3.5 min, pulsations of the same frequency were observed up to 3 km above the radar and at the ground by a disdrometer. High concentrations of large drops were followed by high concentrations of successively smaller drops at the ground. This provides observational evidence to support the recent argument for using a time-varying release of precipitation-sized particles to model observed pulsating rainfall.
Many cases of nonsteady rain from convective clouds displayed repetition periods of between 8 and 25 min.
Abstract
During the Joint Tropical Rain Experiment of the Malaysian Meteorological Service and the University of Toronto, pulsating raindrop ensembles, hereafter pulses, were observed in and around Penang Island. Using a Doppler radar on 25 October 1990, a periodic variation of precipitation aloft 30 km from the radar site, with an approximate 8-min period, was established and seemed to be caused by the evolution and motion of horizontal inhomogeneities existing within the same cell. On 30 October 1990, using a new volume scanning strategy with a repetition cycle of 3.5 min, pulsations of the same frequency were observed up to 3 km above the radar and at the ground by a disdrometer. High concentrations of large drops were followed by high concentrations of successively smaller drops at the ground. This provides observational evidence to support the recent argument for using a time-varying release of precipitation-sized particles to model observed pulsating rainfall.
Many cases of nonsteady rain from convective clouds displayed repetition periods of between 8 and 25 min.
Abstract
For nonconvective, steady light rain with rain rates <5 mm h−1 the mean Doppler velocity of raindrop spectra was found to be constant below the melting band, when the drop-free fall speed was adjusted for pressure. The Doppler radar–weighted raindrop diameters varied from case to case from 1.5 to 2.5 mm while rain rates changed from 1.2 to 2.9 mm h−1. Significant changes of advected velocity moments were observed over periods of 4 min.
These findings were corroborated by three independent systems: a Doppler radar for establishing vertical air speed and mean terminal drop speeds [using extended Velocity Azimuth Display (EVAD) analyses], a Joss–Waldvogel disdrometer at the ground, and a Particle Measuring System (PMS) 2-DP probe flown on an aircraft. These measurements were supported by data from upper-air soundings. The reason why inferred raindrop spectra do not change with height is the negligible interaction rate between raindrops at low rain rates. At low rain rates, numerical box models of drop collisions strongly support this interpretation. It was found that increasing characteristic drop diameters are correlated with increasing rain rates.
Abstract
For nonconvective, steady light rain with rain rates <5 mm h−1 the mean Doppler velocity of raindrop spectra was found to be constant below the melting band, when the drop-free fall speed was adjusted for pressure. The Doppler radar–weighted raindrop diameters varied from case to case from 1.5 to 2.5 mm while rain rates changed from 1.2 to 2.9 mm h−1. Significant changes of advected velocity moments were observed over periods of 4 min.
These findings were corroborated by three independent systems: a Doppler radar for establishing vertical air speed and mean terminal drop speeds [using extended Velocity Azimuth Display (EVAD) analyses], a Joss–Waldvogel disdrometer at the ground, and a Particle Measuring System (PMS) 2-DP probe flown on an aircraft. These measurements were supported by data from upper-air soundings. The reason why inferred raindrop spectra do not change with height is the negligible interaction rate between raindrops at low rain rates. At low rain rates, numerical box models of drop collisions strongly support this interpretation. It was found that increasing characteristic drop diameters are correlated with increasing rain rates.
Abstract
Satellite measurements using the backscattered ultraviolet technique provide a powerful method for the observation of stratospheric ozone. However, rapid input signal variations over three to four orders of magnitude in several minutes can lead to problems with instrument response. Inflight data have recently been used to characterize a “hysteresis” problem on the NOAA-9 SBUV/2 instrument, which affects measurements made shortly after emerging from darkness. Radiance values observed under these conditions can be up to 2%–3% lower than expected. A correction has been derived for NOAA-9 data that is solar zenith angle dependent and varies in amplitude and time. Typical changes to affected polar total ozone values are on the order of 1% but can reach 5% in some cases. Profile ozone changes are altitude dependent, with maximum values of 4%–5% at 1 hPa. The NOAA-11 and NOAA-14 SBUV/2 instruments have a much smaller hysteresis effect than that observed for NOAA-9 SBUV/2 due to a change in photomultiplier tubes. The Nimbus-7 SBUV instrument also shows a hysteresis effect, which has not been fully characterized at this time.
Abstract
Satellite measurements using the backscattered ultraviolet technique provide a powerful method for the observation of stratospheric ozone. However, rapid input signal variations over three to four orders of magnitude in several minutes can lead to problems with instrument response. Inflight data have recently been used to characterize a “hysteresis” problem on the NOAA-9 SBUV/2 instrument, which affects measurements made shortly after emerging from darkness. Radiance values observed under these conditions can be up to 2%–3% lower than expected. A correction has been derived for NOAA-9 data that is solar zenith angle dependent and varies in amplitude and time. Typical changes to affected polar total ozone values are on the order of 1% but can reach 5% in some cases. Profile ozone changes are altitude dependent, with maximum values of 4%–5% at 1 hPa. The NOAA-11 and NOAA-14 SBUV/2 instruments have a much smaller hysteresis effect than that observed for NOAA-9 SBUV/2 due to a change in photomultiplier tubes. The Nimbus-7 SBUV instrument also shows a hysteresis effect, which has not been fully characterized at this time.
Abstract
As the highest mountain on Earth, Mount Everest is an iconic peak that offers an unrivalled natural platform for measuring ongoing climate change across the full elevation range of Asia’s water towers. However, Everest’s extreme environment challenges data collection, particularly on the mountain’s upper slopes, where glaciers accumulate mass and mountaineers are most exposed. Weather stations have operated on Everest before, including the world’s previous highest, but coverage has been sparse in space and time. Here we describe the installation of a network of five automatic weather stations (AWSs), including the two highest stations on Earth (8,430 and 7,945 m MSL) which greatly improves monitoring of this iconic mountain. We highlight sample applications of the new data, including an initial assessment of surface energy fluxes at Camp II (6,464 m MSL) and the South Col (7,945 m MSL), which suggest melt occurs at both sites, despite persistently below-freezing air temperatures. This analysis indicates that melt may even be possible at the 8,850 m MSL summit, and prompts a reevaluation of empirical temperature index models used to simulate glacier melt in the Himalayas that focus only on air temperature. We also provide the first evaluation of numerical weather forecasts at almost 8,000 m MSL and use of model output statistics to reduce forecast error, showcasing an important opportunity to improve climber safety on Everest. Looking forward, we emphasize the considerable potential of these freely available data for understanding weather and climate in the Himalayas and beyond, including tracking the behavior of upper-atmosphere winds, which the AWS network is uniquely positioned to monitor.
Abstract
As the highest mountain on Earth, Mount Everest is an iconic peak that offers an unrivalled natural platform for measuring ongoing climate change across the full elevation range of Asia’s water towers. However, Everest’s extreme environment challenges data collection, particularly on the mountain’s upper slopes, where glaciers accumulate mass and mountaineers are most exposed. Weather stations have operated on Everest before, including the world’s previous highest, but coverage has been sparse in space and time. Here we describe the installation of a network of five automatic weather stations (AWSs), including the two highest stations on Earth (8,430 and 7,945 m MSL) which greatly improves monitoring of this iconic mountain. We highlight sample applications of the new data, including an initial assessment of surface energy fluxes at Camp II (6,464 m MSL) and the South Col (7,945 m MSL), which suggest melt occurs at both sites, despite persistently below-freezing air temperatures. This analysis indicates that melt may even be possible at the 8,850 m MSL summit, and prompts a reevaluation of empirical temperature index models used to simulate glacier melt in the Himalayas that focus only on air temperature. We also provide the first evaluation of numerical weather forecasts at almost 8,000 m MSL and use of model output statistics to reduce forecast error, showcasing an important opportunity to improve climber safety on Everest. Looking forward, we emphasize the considerable potential of these freely available data for understanding weather and climate in the Himalayas and beyond, including tracking the behavior of upper-atmosphere winds, which the AWS network is uniquely positioned to monitor.
Abstract
Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days).
The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.
Abstract
Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days).
The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.
