Search Results
You are looking at 41 - 50 of 50 items for
- Author or Editor: Hai Lin x
- Refine by Access: All Content x
Abstract
Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates in gradient-based variational assimilation if improper error statistics are used. In this paper, two criteria or scoring rules are proposed to quantify the numerical robustness of assimilating a specific set of remote sensing observations and to quantify the reliability of the estimates of the parameters. The criteria are derived by analyzing how the correlations are created via shared observation data and how they may influence the process of variational data assimilation. Experimental tests are conducted and show a good level of agreement with theory. The results illustrate the capability of the criteria to indicate the reliability of the assimilation process. Both criteria can be used with observing system simulation experiments (OSSEs) and in combination with other verification scores.
Abstract
Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates in gradient-based variational assimilation if improper error statistics are used. In this paper, two criteria or scoring rules are proposed to quantify the numerical robustness of assimilating a specific set of remote sensing observations and to quantify the reliability of the estimates of the parameters. The criteria are derived by analyzing how the correlations are created via shared observation data and how they may influence the process of variational data assimilation. Experimental tests are conducted and show a good level of agreement with theory. The results illustrate the capability of the criteria to indicate the reliability of the assimilation process. Both criteria can be used with observing system simulation experiments (OSSEs) and in combination with other verification scores.
Abstract
In this study, detailed characteristics of the leading intraseasonal variability mode of boreal winter surface air temperature (SAT) over the North American (NA) sector are investigated. This intraseasonal SAT mode, characterized by two anomalous centers with an opposite sign—one over central NA and another over east Siberia (ES)/Alaska—bears a great resemblance to the “warm Arctic–cold continent” pattern of the interannual SAT variability over NA. This intraseasonal SAT mode and associated circulation exert a pronounced influence on regional weather extremes, including precipitation over the northwest coast of NA, sea ice concentration over the Chukchi and Bering Seas, and extreme warm and cold events over the NA continent and Arctic region. Surface warming and cooling signals of the intraseasonal SAT mode are connected to temperature anomalies in a deep-tropospheric layer up to 300 hPa with a decreasing amplitude with altitude. Particularly, a coupling between the troposphere and stratosphere is found during evolution of the intraseasonal SAT variability, although whether the stratospheric processes are essential in sustaining the leading intraseasonal SAT mode is difficult to determine based on observations alone. Two origins of wave sources are identified in contributing to vertically propagating planetary waves near Alaska: one over ES/Alaska associated with local intraseasonal variability and another from the subtropical North Pacific via Rossby wave trains induced by tropical convective activity over the western Pacific, possibly associated with the Madden–Julian oscillation.
Abstract
In this study, detailed characteristics of the leading intraseasonal variability mode of boreal winter surface air temperature (SAT) over the North American (NA) sector are investigated. This intraseasonal SAT mode, characterized by two anomalous centers with an opposite sign—one over central NA and another over east Siberia (ES)/Alaska—bears a great resemblance to the “warm Arctic–cold continent” pattern of the interannual SAT variability over NA. This intraseasonal SAT mode and associated circulation exert a pronounced influence on regional weather extremes, including precipitation over the northwest coast of NA, sea ice concentration over the Chukchi and Bering Seas, and extreme warm and cold events over the NA continent and Arctic region. Surface warming and cooling signals of the intraseasonal SAT mode are connected to temperature anomalies in a deep-tropospheric layer up to 300 hPa with a decreasing amplitude with altitude. Particularly, a coupling between the troposphere and stratosphere is found during evolution of the intraseasonal SAT variability, although whether the stratospheric processes are essential in sustaining the leading intraseasonal SAT mode is difficult to determine based on observations alone. Two origins of wave sources are identified in contributing to vertically propagating planetary waves near Alaska: one over ES/Alaska associated with local intraseasonal variability and another from the subtropical North Pacific via Rossby wave trains induced by tropical convective activity over the western Pacific, possibly associated with the Madden–Julian oscillation.
Abstract
The total suspended particulate (TSP) samples over the Bohai Sea and the Yellow Sea were collected during two cruises in spring and autumn in 2012. Concentrations of water-soluble ions {Na+, K+, NH4 +, Mg2+, Ca2+, Cl−, NO3 −, SO4 2−, and CH3SO3 − [methanesulfonic acid (MSA)]} and trace metals (Al, Pb, Zn, Cd, Cu, and V) were measured. Mass concentrations of TSP samples ranged from 65.2 to 136 μg m−3 in spring and from 15.9 to 70.3 μg m−3 in autumn, with average values of 100 ± 22.4 and 40.2 ± 17.8 μg m−3, respectively. The aerosol was acidic throughout the sampling periods according to calculation of equivalent concentrations of the cations (NH4 +, nss-Ca2+, and nss-K+) and anions (nss-SO4 2− and NO3 −). Correlation analysis and enrichment factors revealed that the aerosol composition in the coastal marine atmosphere had a feature of a mixture of air masses: that is, crustal, marine, and anthropogenic emissions. Trace metals were enriched by a wide range of 1–103, and enrichment factors for crustal source (EFc) were relatively higher in spring. Species like Cd, Zn, and Pb had an overwhelming contribution from anthropogenic sources. In addition, the concentrations of MSA varied from 0.0075 to 0.17 and from 0.0019 to 0.018 μg m−3 during the spring and autumn cruises, respectively, with means of 0.061 and 0.012 μg m−3, respectively. Based on the observed MSA and nss-SO4 2− concentrations in spring and autumn, the relative biogenic sulfur contributions to nss-SO4 2− were estimated to be 8.0% and 3.5% on average, respectively, implying that anthropogenic sources had a dominant contribution to the sulfur budget over the observational area.
Abstract
The total suspended particulate (TSP) samples over the Bohai Sea and the Yellow Sea were collected during two cruises in spring and autumn in 2012. Concentrations of water-soluble ions {Na+, K+, NH4 +, Mg2+, Ca2+, Cl−, NO3 −, SO4 2−, and CH3SO3 − [methanesulfonic acid (MSA)]} and trace metals (Al, Pb, Zn, Cd, Cu, and V) were measured. Mass concentrations of TSP samples ranged from 65.2 to 136 μg m−3 in spring and from 15.9 to 70.3 μg m−3 in autumn, with average values of 100 ± 22.4 and 40.2 ± 17.8 μg m−3, respectively. The aerosol was acidic throughout the sampling periods according to calculation of equivalent concentrations of the cations (NH4 +, nss-Ca2+, and nss-K+) and anions (nss-SO4 2− and NO3 −). Correlation analysis and enrichment factors revealed that the aerosol composition in the coastal marine atmosphere had a feature of a mixture of air masses: that is, crustal, marine, and anthropogenic emissions. Trace metals were enriched by a wide range of 1–103, and enrichment factors for crustal source (EFc) were relatively higher in spring. Species like Cd, Zn, and Pb had an overwhelming contribution from anthropogenic sources. In addition, the concentrations of MSA varied from 0.0075 to 0.17 and from 0.0019 to 0.018 μg m−3 during the spring and autumn cruises, respectively, with means of 0.061 and 0.012 μg m−3, respectively. Based on the observed MSA and nss-SO4 2− concentrations in spring and autumn, the relative biogenic sulfur contributions to nss-SO4 2− were estimated to be 8.0% and 3.5% on average, respectively, implying that anthropogenic sources had a dominant contribution to the sulfur budget over the observational area.
Abstract
A two-way interactive, nested-grid system tested with The Pennsylvania Slate University/INCAR three-dimensional mesoscale model is described. A mesh structure, designed to minimize numerical noise, together with a procedure for obtaining compatible coarse grid mesh (COM) and fine grid mesh (FOM) terrain conditions, is presented. Also, a method to initialize the nested-grid meshes is proposed. The nested-grid system has been tested with real data and raw terrain under different severe conditions. A 12-h simulation of a propagating jet streak over complex terrain is presented; the results indicate relatively noise-free solutions on both the OGM and FGM domains.
Abstract
A two-way interactive, nested-grid system tested with The Pennsylvania Slate University/INCAR three-dimensional mesoscale model is described. A mesh structure, designed to minimize numerical noise, together with a procedure for obtaining compatible coarse grid mesh (COM) and fine grid mesh (FOM) terrain conditions, is presented. Also, a method to initialize the nested-grid meshes is proposed. The nested-grid system has been tested with real data and raw terrain under different severe conditions. A 12-h simulation of a propagating jet streak over complex terrain is presented; the results indicate relatively noise-free solutions on both the OGM and FGM domains.
Abstract
Dynamical monthly prediction at the Canadian Meteorological Centre (CMC) was produced as part of the seasonal forecasting system over the past two decades. A new monthly forecasting system, which has been in operation since July 2015, is set up based on the operational Global Ensemble Prediction System (GEPS). This monthly forecasting system is composed of two components: 1) the real-time forecast, where the GEPS is extended to 32 days every Thursday; and 2) a 4-member hindcast over the past 20 years, which is used to obtain the model climatology to calibrate the monthly forecast. Compared to the seasonal prediction system, the GEPS-based monthly forecasting system takes advantage of the increased model resolution and improved initialization.
Forecasts of the past 2-yr period (2014 and 2015) are verified. Analysis is performed separately for the winter half-year (November–April), and the summer half-year (May–October). Weekly averages of 2-m air temperature (T2m) and 500-hPa geopotential height (Z500) are assessed. For Z500 in the Northern Hemisphere, limited skill can be found beyond week 2 (days 12–18) in summer, while in winter some skill exists over the Pacific and North American region beyond week 2. For T2m in North America, significant skill is found over a large part of the continent all the way to week 4 (days 26–32). The distribution of the wintertime T2m skill in North America is consistent with the influence of the Madden–Julian oscillation, indicating that a significant part of predictability likely comes from the tropics.
Abstract
Dynamical monthly prediction at the Canadian Meteorological Centre (CMC) was produced as part of the seasonal forecasting system over the past two decades. A new monthly forecasting system, which has been in operation since July 2015, is set up based on the operational Global Ensemble Prediction System (GEPS). This monthly forecasting system is composed of two components: 1) the real-time forecast, where the GEPS is extended to 32 days every Thursday; and 2) a 4-member hindcast over the past 20 years, which is used to obtain the model climatology to calibrate the monthly forecast. Compared to the seasonal prediction system, the GEPS-based monthly forecasting system takes advantage of the increased model resolution and improved initialization.
Forecasts of the past 2-yr period (2014 and 2015) are verified. Analysis is performed separately for the winter half-year (November–April), and the summer half-year (May–October). Weekly averages of 2-m air temperature (T2m) and 500-hPa geopotential height (Z500) are assessed. For Z500 in the Northern Hemisphere, limited skill can be found beyond week 2 (days 12–18) in summer, while in winter some skill exists over the Pacific and North American region beyond week 2. For T2m in North America, significant skill is found over a large part of the continent all the way to week 4 (days 26–32). The distribution of the wintertime T2m skill in North America is consistent with the influence of the Madden–Julian oscillation, indicating that a significant part of predictability likely comes from the tropics.
Abstract
We evaluate the soil moisture hindcasts and the reconstruction runs giving the hindcasts initial conditions in version 2.1 of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2.1). Different strategies are used to initialize the hindcasts for the two CanSIPSv2.1 models, CanCM4i and GEM5-NEMO, with contrasting impacts on the soil moisture initial conditions and forecast performance. Forecast correlation skill is decomposed into contributions from persistence of the initial anomalies and contributions not linked to persistence, with performance largely driven by the accuracy of the initial conditions in regions of strong persistence. Seasonal soil moisture correlation skill is significant for several months into the hindcasts depending on initial and target months, with contributions not linked to persistence becoming more notable at longer lead times. For the first 2-4 months, the quality of CanSIPSv2.1 ensemble mean forecasts tend to be higher on average during summer and fall, and is comparable to that of the best performing model, whereas CanSIPSv2.1 outperforms the single models during spring and winter. For longer lead times, remote climate influences from the Pacific Ocean are notable and contribute to predictable soil moisture variability in teleconnected regions.
Abstract
We evaluate the soil moisture hindcasts and the reconstruction runs giving the hindcasts initial conditions in version 2.1 of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2.1). Different strategies are used to initialize the hindcasts for the two CanSIPSv2.1 models, CanCM4i and GEM5-NEMO, with contrasting impacts on the soil moisture initial conditions and forecast performance. Forecast correlation skill is decomposed into contributions from persistence of the initial anomalies and contributions not linked to persistence, with performance largely driven by the accuracy of the initial conditions in regions of strong persistence. Seasonal soil moisture correlation skill is significant for several months into the hindcasts depending on initial and target months, with contributions not linked to persistence becoming more notable at longer lead times. For the first 2-4 months, the quality of CanSIPSv2.1 ensemble mean forecasts tend to be higher on average during summer and fall, and is comparable to that of the best performing model, whereas CanSIPSv2.1 outperforms the single models during spring and winter. For longer lead times, remote climate influences from the Pacific Ocean are notable and contribute to predictable soil moisture variability in teleconnected regions.
Abstract
The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.
Abstract
The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.
Abstract
This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden–Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.
Abstract
This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden–Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.
Abstract
The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.
Abstract
The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.
