A Statistical Intra-Seasonal Prediction Model of Extended Boreal Summer Western North Pacific Tropical Cyclone Genesis

A L2 regularized logistic regression model is developed in this study to predict weekly tropical cyclone (TC) genesis over the western North Pacific (WNP) and sub-regions of the WNP including the South China Sea (SCS), the western WNP (WWNP), and the eastern WNP (EWNP). The potential predictors for the TC genesis model include a time-varying TC genesis climatology, the Madden-Julian oscillation (MJO), the quasi-biweekly oscillation (QBWO), and ENSO. The relative importance of the predictors in a constructed L2 regression model is justified by a forward stepwise selection procedure for each region from a 0-week to a 7-week lead. Cross-validated hindcasts are then generated for the corresponding prediction schemes out to a 7-week lead. The TC genesis climatology generally improves the regional model skill, while the importance of intra-seasonal oscillations and ENSO are regionally dependent. Over the WNP, there is increased model skill over the time-varying climatology in predicting weekly TC genesis out to a 4-week lead by including the MJO and QBWO, while ENSO has a limited impact. On a regional scale, ENSO and then the MJO and QBWO respectively, are the two most important predictors over the EWNP and WWNP after the TC genesis climatology. The MJO is found to be the most important predictor over the SCS. The logistic regression model is shown to have comparable reliability and forecast skill scores to the ECMWF dynamical model on intra-seasonal time scales.

A hydrological model skill score and revised R-squared

Despite the advances in methods of statistical and mathematical modeling, there is considerable lack of focus on improving how to judge models’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric in modelling and prediction of environmental systems. However, known issues of R2 are that it: (i) can be low and high for an accurate and imperfect model, respectively; (ii) yields the same value when we regress observed on modelled series and vice versa; and (iii) does not quantify a model's bias (B). A new model skill score E and revised R-squared (RRS) are presented to combine correlation, term B and capacity to capture variability. Differences between E and RRS lie in the forms of correlation and the term B used for each metric. Acceptability of E and RRS was demonstrated through comparison of results from a large number of hydrological simulations. By applying E and RRS, the modeller can diagnostically identify and expose systematic issues behind model optimizations based on other ‘goodness-of-fits’ such as Nash–Sutcliffe efficiency (NSE) and mean squared error. Unlike NSE, which varies from −∞ to 1, E and RRS occur over the range 0–1. MATLAB codes for computing E and RRS are provided.

The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

PENGEMBANGAN ALAT BANTU PENYUSUNAN DAN BIMBINGAN KARYA ILMIAH DENGAN MODEL SKILL TREE

ABSTRACT:This study aims to: (1) produce a product in the form of an android application as a tool for the preparation of scientific papers using the skill tree model; (2) knowing the results of the software performance that using the skill tree model. This research is development research with a combined method of the waterfall method and Dick and Carey. This study focuses on software functionality so that testing is carried out by software quality control. Research data obtained through observation, interviews, a document study, and questionnaires. The results of this study are: (1) the product is developed by applying the skill tree model in an android application designed using Android Studio; (2) the test results by quality control, seen from the aspects of functionality, reliability, efficiency, maintainability, and portability, obtained a percentage of 76.9% (Very Good) for supervisor and 77.8% (Very Good) for teacher applications.ABSTRAK:Penelitian ini bertujuan untuk: (1) menghasilkan produk berupa aplikasi android sebagai alat bantu penyusunan karya ilmiah dengan model skill tree; (2) mengetahui hasil unjuk kerja software yang menggunakan model skill tree. Penelitian ini merupakan penelitian pengembangan dengan metode gabungan antara metode waterfall dan Dick and Carey. Penelitian ini memberikan fokus pada fungsionalitas perangkat lunak sehingga pengujian dilakukan oleh quality control perangkat lunak. Data penelitian diperoleh melalui observasi, wawancara, studi dokumen, dan angket. Hasil penelitian ini adalah sebagai berikut: (1) produk dikembangkan dengan menerapkan model skill tree dalam aplikasi android yang dirancang menggunakan Android Studio; (2) hasil pengujian oleh quality control dilihat dari aspek functionality, reliability, efficiency, maintainability, dan portability didapat persentase 76,9% (Sangat Baik) untuk pembimbing dan 77,8% (Sangat Baik) untuk aplikasi guru.

A global model perturbed parameter ensemble study of secondary organic aerosol formation

A global model perturbed parameter ensemble of 60 simulations was used to explore how combinations of six parameters related to secondary organic aerosol (SOA) formation affect particle number concentrations and organic aerosol mass. The parameters represent the formation of organic compounds with different volatilities from biogenic and anthropogenic sources. The most plausible parameter combinations were determined by comparing the simulations against observations of the number concentration of particles larger than 3 nm diameter (N3), the number concentration of particles larger than 50 nm diameter (N50), and the organic aerosol (OA) mass concentration. The simulations expose a high degree of model equifinality in which the skill of widely different parameter combinations cannot be distinguished against observations. We therefore conclude that, based on the observations we have used, a six-parameter SOA scheme is under-determined. Nevertheless, the model skill in simulating N3 and N50 is clearly determined by the low-volatility and extremely low-volatility compounds that affect new particle formation and growth, and the skill in simulating OA mass is determined by the low-volatility and semi-volatile compounds. The biogenic low-volatility class of compounds that grow nucleated clusters and condense on all particles is found to have the strongest effect on the model skill in simulating N3, N50, and OA. The simulations also expose potential structural deficiencies in the model: we find that parameter combinations that are best for N3 and N50 are worst for OA mass, and the ensemble exaggerates the observed seasonal cycle of particle concentrations – a deficiency that we conclude requires an additional anthropogenic source of either primary or secondary particles.

The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Volcanic eruptions can cause significant disruption to society and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3° N, 153.2° E). The eruption emitted around 1.5 ± 0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the Tropospheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption, but is unable, in its standard configuration, to capture the extent and precise location of very high-concentration regions within the volcanic cloud. Using the Fractional Skill Score as metric for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 days after the eruption where vertical column densities (VCD) of SO2 (in Dobson Units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 days only. The lower skill for these high-concentration regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard diffusion parameters used in NAME by a factor of four results in a slightly increased model skill during the first five days of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. We find that the temporal evolution of the NH-mean SO2 mass burden simulated by NAME strongly depends on the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), both NAME and TROPOMI show a similar peak in SO2 mass burden (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 days in the NH. Our work demonstrates the large potential of using high-resolution satellite retrievals to identify and rectify limitations in dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

A global model perturbed parameter ensemble study of secondary organic aerosol formation

A global model perturbed parameter ensemble of 60 simulations was used to explore how combinations of six parameters related to secondary organic aerosol (SOA) formation affect particle number concentrations and organic aerosol mass. The parameters represent the formation of organic compounds with different volatilities from biogenic and anthropogenic sources. The most plausible parameter combinations were determined by comparing the simulations against observations of the number concentration of particles larger than 3 nm diameter (N3), the number concentration of particles larger than 50 nm diameter (N50), and the organic aerosol (OA) mass concentration. The simulations expose a high degree of model equifinality in which the skill of widely different parameter combinations cannot be distinguished against observations. We therefore conclude that, based on the observations we have used, a 6-parameter SOA scheme is under-determined. Nevertheless, the model skill in simulating N3 and N50 is clearly determined by the low and extremely low volatility compounds that affect new particle formation and growth, and the skill in simulating OA mass is determined by the low volatility and semi-volatile compounds. The biogenic low volatility class of compounds that grow nucleated clusters and condense on all particles is found to have the strongest effect on the model skill in simulating N3, N50 and OA. The simulations also expose potential structural deficiencies in the model: we find that parameter combinations that are best for N3 and N50 are worst for OA mass, and the ensemble exaggerates the observed seasonal cycle of particle concentrations – a deficiency that we conclude requires an additional anthropogenic source of either primary or secondary particles.

An analysis of air pollution at some industrial areas of Kano using the AERMOD Model

The effect of pollution on air quality has been a concern for mankind for a long time. In some cases the problem is essentially one of local emissions in a given urban area leading to an adverse effect on air quality in that same area. However, in the general case, the problem is more diverse in that the problem of air pollution has multiplicity effects beyond the point source and these effects are dynamic in nature. Such effects are usually evaluated using dynamical equations. In this study, a comprehensive review on effect of air polluting variables was described on the basis of evaluation of formulation equations of the American Meteorological Society and U.S. Environmental protection Agency Regulatory Model (AERMOD view 9.6.5). The AERMOD model was also used to simulate the dispersion and deposition of the hourly and daily H2S and NO2 concentrations from two domains: Challawa and Sharada industrial estates /areas respectively. The AERMOD model evaluation showed that there was good correlation between the modelled and observed H2S concentration for the daily and hourly comparison at Challawa (0.53 and 0.91 respectively) but the daily and hourly comparison of H2S at Sharada (0.13 and 0.46 respectively) was seen to drop indicating poor correlation and model skill. However, model evaluation of NO2 shows poor agreements and model skill at Challawa as well as daily comparison at Sharada. However, the modelling shows good agreement (R2= 0.64) in the trend for the hourly value modelled versus observed concentrations at Sharada. Moreover, the mean absolute percentage error (MAPE) for the two pollutants (H2S and NO2) at all the two domains indicates highly accurate result for both daily and hourly concentrations. AERMOD software can therefore be used to estimate the dispersion and deposition of the pollutants at some domains considered in this study. Key Words: AERMOD model, Air pollutant, Industrial sources, Dispersion and Deposition

Evaluation of CMIP6 Model Precipitation Variability Through Compositing in Cloud-defined Weather States

<p>In order to understand the mechanisms determining precipitation variability and to evaluate model skill in simulating those mechanisms, it is important to partition the precipitation field into regimes that include distinct sets of processes. In the past, dynamic fields like omega and SLP have been used to define regimes and study cloud, radiation, and precipitation variability. More recently, cloud-defined weather states were derived and used for similar analyses. Here, we apply a new cloud-defined Weather State dataset derived from the higher-resolution ISCCP-H data to examine precipitation variability at global scales and evaluate CMIP6 model precipitation simulations . In addition, precipitation partitioning using mid-tropospheric vertical velocity is performed, and the differences between the results of the two compositing methodologies are discussed.</p>