scholarly journals A Diurnal Predictability Barrier for Weather Forecasts

2021 ◽  
Author(s):  
Peng Wang ◽  
Yishuai Jin ◽  
Zhengyu Liu
Keyword(s):  
Growth Rate ◽  
Diurnal Cycle ◽  
Southern Oscillation ◽  
Initial Time ◽  
Seasonal Predictability ◽  
Weather Forecasts ◽  
Idealized Model ◽  
Hourly Data ◽  
New Feature ◽  
Beijing China

AbstractIn this study, we investigate a diurnal predictability barrier (DPB) for weather predictions using an idealized model and observations. This DPB is referred to a maximum drop of predictability (e.g., autocorrelation) at a particular time of the day, regardless of the initial time. Previous studies demonstrated that a strong seasonal cycle of El Niño-Southern Oscillation (ENSO) growth rate is responsible for the seasonal predictability barrier of the ENSO in spring. This led us to investigate whether or not a strong diurnal cycle may generate a DPB. We study the DPB using an idealized model, the Lorenz 1963 model (Lorenz63), with the addition of a diurnal cycle. We find that diurnal growth rate can generate a DPB in this chaotic system, regardless of the initial error. Finally, by calculating the autocorrelation function using the hourly data of surface temperature, we explore the DPB at two stations in Wisconsin, USA and Beijing, China. A clear DPB feature is found at both stations. The dramatic drop of predictability at a specific time of the day is likely due to the diurnal variation of the system. This is a new feature that needs further study for short-term weather predictions.

scholarly journals Atmospheric PM2.5 Prediction Using DeepAR Optimized by Sparrow Search Algorithm with Opposition-Based and Fitness-Based Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 894
Author(s):  
Feng Jiang ◽  
Xingyu Han ◽  
Wenya Zhang ◽  
Guici Chen
Keyword(s):  
Search Algorithm ◽  
Recurrent Network ◽  
Learning Method ◽  
Probabilistic Forecasting ◽  
Atmospheric Concentration ◽  
Opposition Based Learning ◽  
Hourly Data ◽  
Beijing China ◽  
Important Significance ◽  
Pm2.5 Concentration

There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition, the improved Sparrow Search Algorithm (FOSSA) is employed to optimize the initial weights of probabilistic forecasting model with autoregressive recurrent network (DeepAR). Then, the FOSSA–DeepAR learning method is utilized to achieve the point prediction and interval prediction of PM2.5 concentration in Beijing, China. The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model. Furthermore, hourly data of PM2.5 and O3 concentration in Taian of China, O3 concentration in Beijing, China are used to verify the effectiveness and robustness of the proposed FOSSA–DeepAR learning method. Finally, the empirical results illustrate that the proposed FOSSA–DeepAR learning model can achieve more efficient and accurate predictions in both interval and point prediction.

scholarly journals Limited impact of El Niño–Southern Oscillation on variability and growth rate of atmospheric methane

2018 ◽  
Vol 15 (21) ◽  
pp. 6371-6386 ◽  
Author(s):  
Hinrich Schaefer ◽  
Dan Smale ◽  
Sylvia E. Nichol ◽  
Tony M. Bromley ◽  
Gordon W. Brailsford ◽  
...  
Keyword(s):  
Climate Change ◽  
Growth Rate ◽  
Biomass Burning ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Atmospheric Methane ◽  
Ch4 Production ◽  
A Minor

Abstract. The El Niño–Southern Oscillation (ENSO) has been suggested as a strong forcing in the methane cycle and as a driver of recent trends in global atmospheric methane mole fractions [CH4]. Such a sensitivity of the global CH4 budget to climate events would have important repercussions for climate change mitigation strategies and the accuracy of projections for future greenhouse forcing. Here, we test the impact of ENSO on atmospheric CH4 in a correlation analysis. We use local and global records of [CH4], as well as stable carbon isotopic records of atmospheric CH4 (δ13CH4), which are particularly sensitive to the combined ENSO effects on CH4 production from wetlands and biomass burning. We use a variety of nominal, smoothed, and detrended time series including growth rate records. We find that at most 36 % of the variability in [CH4] and δ13CH4 is attributable to ENSO, but only for detrended records in the southern tropics. Trend-bearing records from the southern tropics, as well as all studied hemispheric and global records, show a minor impact of ENSO, i.e. < 24 % of variability explained. Additional analyses using hydrogen cyanide (HCN) records show a detectable ENSO influence on biomass burning (up to 51 %–55 %), suggesting that it is wetland CH4 production that responds less to ENSO than previously suggested. Dynamics of the removal by hydroxyl likely counteract the variation in emissions, but the expected isotope signal is not evident. It is possible that other processes obscure the ENSO signal, which itself indicates a minor influence of the latter on global CH4 emissions. Trends like the recent rise in atmospheric [CH4] can therefore not be attributed to ENSO. This leaves anthropogenic methane sources as the likely driver, which must be mitigated to reduce anthropogenic climate change.

scholarly journals Impact of El Niño Southern Oscillation on the interannual variability of methane and tropospheric ozone

2019 ◽  
Author(s):  
Matthew J. Rowlinson ◽  
Alexandru Rap ◽  
Stephen R. Arnold ◽  
Richard J. Pope ◽  
Martyn P. Chipperfield ◽  
...  
Keyword(s):  
Growth Rate ◽  
Interannual Variability ◽  
Biomass Burning ◽  
El Niño ◽  
Atmospheric Transport ◽  
El Nino ◽  
Southern Oscillation ◽  
Fire Emissions ◽  
Tropospheric O3 ◽  
Global Mean

Abstract. The growth rate of global methane (CH4) concentrations has a strong interannual variability which is believed to be driven largely by fluctuations in CH4 emissions from wetlands and wildfires, as well as changes to the atmospheric sink. The El Niño Southern Oscillation (ENSO) is known to influence fire occurrence, wetland emission and atmospheric transport, but there are still important uncertainties associated with the exact mechanism and magnitude of this influence. Here we use a modelling approach to investigate how fires and meteorology control the interannual variability of global carbon monoxide (CO), CH4 and ozone (O3) concentrations, particularly during large El Niño events. Using a three-dimensional chemical transport model (TOMCAT) coupled to a sophisticated aerosol microphysics scheme (GLOMAP) we simulate changes to CO, hydroxyl radical (OH) and O3 for the period 1997–2014. We then use an offline radiative transfer model to quantify the impact of changes to atmospheric composition as a result of specific drivers. During the El Niño event of 1997–1998, there were increased emissions from biomass burning globally. As a result, global CO concentrations increased by more than 40 %. This resulted in decreased global mass-weighted tropospheric OH concentrations of up to 9 % and a resulting 4 % increase in the CH4 atmospheric lifetime. The change in CH4 lifetime led to a 7.5 ppb yr−1 increase in global mean CH4 growth rate in 1998. Therefore biomass burning emission of CO could account for 72 % of the total effect of fire emissions on CH4 growth rate in 1998. Our simulations indicate variations in fire emissions and meteorology associated with El Niño have opposing impacts on tropospheric O3 burden. El Niño-related atmospheric transport changes decrease global tropospheric O3 concentrations leading to a −0.03 Wm−2 change in O3 radiative effect (RE). However, enhanced fire emission of precursors such as nitrous oxides (NOx) and CO increase O3 RE by 0.03 Wm−2. While globally the two mechanisms nearly cancel out, causing only a small change in global mean O3 RE, the regional changes are large   up to −0.33 Wm−2 with potentially important consequences for atmospheric heating and dynamics.

scholarly journals The Functional Spatio-Temporal Statistical Model with Application to O3 Pollution in Beijing, China

2020 ◽  
Vol 17 (9) ◽  
pp. 3172
Author(s):  
Yaqiong Wang ◽  
Ke Xu ◽  
Shaomin Li
Keyword(s):  
Statistical Model ◽  
Diurnal Cycle ◽  
Monitoring Network ◽  
Temporal Correlation ◽  
Ground Level ◽  
Quality Data ◽  
Ozone Pollution ◽  
Spatial Correlation Structure ◽  
Spatio Temporal ◽  
Beijing China

In recent years, with rapid industrialization and massive energy consumption, ground-level ozone ( O 3 ) has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of O 3 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to O 3 pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.

scholarly journals Probabilistic Evaluation of Tibetan Plateau Mesoscale Vortex on 18 July 2013

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Haiwen Liu ◽  
Kaijun Wu ◽  
Mengxing Du ◽  
Ning Fu
Keyword(s):  
Tibetan Plateau ◽  
Heavy Rainfall ◽  
Western Pacific Subtropical High ◽  
Initial Time ◽  
Baikal Lake ◽  
Mesoscale Vortex ◽  
Weather Forecasts ◽  
Probabilistic Evaluation ◽  
The Sichuan Basin

Tibetan Plateau (TP) mesoscale vortex (TPMV) was regarded as one of the most important rain bearing systems in China. Previous studies focused on the mechanisms of the TPMV in the viewpoint of deterministic forecast; however, few studies investigate the predictability of the TPMV using the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) from the European Center for Medium Range Weather Forecasts (ECWMF). This paper investigates the location and the intensity of the larger-scale synoptic systems that influenced the development of the TPMV and its associated heavy rainfall by correlation and composite analysis. The case study on 18 July 2013 shows that stronger Balkhash Lake ridge, weaker Baikal Lake trough, and weaker western Pacific subtropical high (WPSH) are favorable to formation of TPMV over the Sichuan basin (SCB); otherwise, weaker Balkhash Lake ridge, stronger Baikal Lake trough, and stronger WPSH result in formation of TPMV to west of the SCB slightly. After the initial time, forecast for next 48 h of the geopotential height over the SCB can be viewed as a precursor of the subsequent time-averaged 90–108 h forecast of TPMV. TPMV had critical contributions to the heavy rainfall over the SCB on 18 July 2013.

scholarly journals Verification of NWP Model Analyses and Radiosonde Humidity Data with GPS Precipitable Water Vapor Estimates during AMMA

2009 ◽  
Vol 24 (4) ◽  
pp. 1085-1101 ◽  
Author(s):  
O. Bock ◽  
M. Nuret
Keyword(s):  
Water Vapor ◽  
Diurnal Cycle ◽  
Bias Correction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Precipitation Forecast ◽  
Weather Forecasts ◽  
Multidisciplinary Analysis ◽  
Correction Scheme ◽  
Nwp Model

Abstract This paper assesses the performance of the European Centre for Medium-Range Weather Forecasts-Integrated Forecast System (ECMWF-IFS) operational analysis and NCEP–NCAR reanalyses I and II over West Africa, using precipitable water vapor (PWV) retrievals from a network of ground-based GPS receivers operated during the African Monsoon Multidisciplinary Analysis (AMMA). The model analyses show reasonable agreement with GPS PWV from 5-daily to monthly means. Errors increase at shorter time scales, indicating that these global NWP models have difficulty in handling the diurnal cycle and moist processes at the synoptic scale. The ECMWF-IFS analysis shows better agreement with GPS PWV than do the NCEP–NCAR reanalyses (the RMS error is smaller by a factor of 2). The model changes in ECMWF-IFS were not clearly reflected in the PWV error over the period of study (2005–08). Radiosonde humidity biases are diagnosed compared to GPS PWV. The impacts of these biases are evidenced in all three model analyses at the level of the diurnal cycle. The results point to a dry bias in the ECMWF analysis in 2006 when Vaisala RS80-A soundings were assimilated, and a diurnally varying bias when Vaisala RS92 or Modem M2K2 soundings were assimilated: dry during day and wet during night. The overall bias is offset to wetter values in NCEP–NCAR reanalysis II, but the diurnal variation of the bias is observed too. Radiosonde bias correction is necessary to reduce NWP model analysis humidity biases and improve precipitation forecast skill. The study points to a wet bias in the Vaisala RS92 data at nighttime and suggests that caution be used when establishing a bias correction scheme.

scholarly journals Changes in Seasonal Predictability due to Global Warming

2014 ◽  
Vol 27 (1) ◽  
pp. 300-311 ◽  
Author(s):  
Timothy DelSole ◽  
Xiaoqin Yan ◽  
Paul A. Dirmeyer ◽  
Mike Fennessy ◽  
Eric Altshuler
Keyword(s):  
North Atlantic ◽  
Southern Oscillation ◽  
Teleconnection Pattern ◽  
Future Climate ◽  
Quantitative Relation ◽  
Seasonal Predictability ◽  
Climate System Model ◽  
The North ◽  
The Tropics ◽  
Standard Techniques

Abstract The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Niño–Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including 1) deriving a quantitative relation between predictability of the first and second kinds; 2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and 3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.

scholarly journals Poleward Atmospheric Energy Transports and Their Variability as Evaluated from ECMWF Reanalysis Data

2012 ◽  
Vol 25 (2) ◽  
pp. 734-752 ◽  
Author(s):  
Michael Mayer ◽  
Leopold Haimberger
Keyword(s):  
Energy Budget ◽  
Energy Transport ◽  
Southern Oscillation ◽  
Forecast Model ◽  
Reanalysis Data ◽  
Energy Budgets ◽  
Global Energy ◽  
Weather Forecasts ◽  
Short Period ◽  
Global Energy Budget

Abstract The vertically integrated global energy budget is evaluated with a direct and an indirect method (both corrected for mass inconsistencies of the forecast model), mainly using the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) data. A new estimate for the net poleward total energy transport is given. Comparison to satellite-derived radiation data proves that ERA-Interim is better suited for investigation of interannual variations of the global energy budget than available satellite data since these either cover a relatively short period of time or are too inhomogeneous in time. While much improved compared to the 40-yr ECMWF Re-Analysis (ERA-40), regionally averaged energy budgets of ERA-Interim show that strong anomalies of forecasted vertical fluxes tend to be partly compensated by unrealistically large forecasted energy storage rates. Discrepancies between observed and forecasted monthly mean tendencies can be taken as rough measure for the uncertainties involved in the ERA-Interim energy budget. El Niño–Southern Oscillation (ENSO) is shown to have large impact on regional energy budgets, but strong compensation occurs between the western and eastern Pacific, leading to only small net variations of the total poleward energy transports (similar magnitude as the uncertainty of the computations). However, Hovmöller longitude–time plots of tropical energy exports show relatively strong slowly eastward-moving poleward transport anomalies in connection with ENSO. Verification of these findings using independent estimates still needs to be done.

scholarly journals Improving statistical prediction and revealing nonlinearity of ENSO using observations of ocean heat content in the tropical Pacific

2021 ◽  
Author(s):  
Aleksei Seleznev ◽  
Dmitry Mukhin
Keyword(s):  
Southern Oscillation ◽  
Nonlinear Models ◽  
Heat Content ◽  
Optimization Method ◽  
Tropical Pacific ◽  
Ocean Heat Content ◽  
Bayesian Optimization ◽  
Statistical Prediction ◽  
Seasonal Predictability ◽  
The Tropical Pacific

Abstract It is well-known that the upper ocean heat content (OHC) variability in the tropical Pacific contains valuable information about dynamics of El Niño–Southern Oscillation (ENSO). Here we combine sea surface temperature (SST) and OHC indices derived from the gridded datasets to construct a phase space for data-driven ENSO models. Using a Bayesian optimization method, we construct linear as well as nonlinear models for these indices. We find that the joint SST-OHC optimal models yield significant benefits in predicting both the SST and OHC as compared with the separate SST or OHC models. It is shown that these models substantially reduces seasonal predictability barriers in each variable – the spring barrier in the SST index and the winter barrier in the OHC index. We also reveal the significant nonlinear relationships between the ENSO variables manifesting on interannual scales, which opens prospects for improving yearly ENSO forecasting.

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