Denton’s Temporal Disaggregation Methods with the Application of Bangladesh’s Annual Gross Domestic Product for Quarterly Benchmarking and Forecasting

<p>Different methods of temporal disaggregation are discussed in detail; mainly the methods developed by Denton in 1971 and other purely mathematical methods. First, the original method developed by Denton and its solution are described by referencing Denton’s original article. The Cholette–Dagum regression-based method (or Denton-Cholette method) is also included to enrich the comparison. Bangladesh’s annual export figures are then disaggregated into a quarterly series by Denton’s additive and proportional (first and second difference) methods, and the Denton-Cholette additive and proportional (first and second difference) methods by using R package “tempdisagg”. The quarterly imports of capital goods and others (Iron, steel and other base metals; and capital machinery) in Bangladesh are used as the indicator series for the fiscal years FY2009 to FY2019. By comparing the estimated series with the real quarterly exports series, with the aid of root mean squared errors, it is concluded that the Denton-Cholette additive method (the first difference) performs better than the Denton-Cholette’s proportional variants as well as Denton’s additive and proportional variants.<b></b></p>

Denton’s Temporal Disaggregation Methods with the Application of Bangladesh’s Annual Gross Domestic Product for Quarterly Benchmarking and Forecasting

<p>Different methods of temporal disaggregation are discussed in detail; mainly the methods developed by Denton in 1971 and other purely mathematical methods. First, the original method developed by Denton and its solution are described by referencing Denton’s original article. The Cholette–Dagum regression-based method (or Denton-Cholette method) is also included to enrich the comparison. Bangladesh’s annual export figures are then disaggregated into a quarterly series by Denton’s additive and proportional (first and second difference) methods, and the Denton-Cholette additive and proportional (first and second difference) methods by using R package “tempdisagg”. The quarterly imports of capital goods and others (Iron, steel and other base metals; and capital machinery) in Bangladesh are used as the indicator series for the fiscal years FY2009 to FY2019. By comparing the estimated series with the real quarterly exports series, with the aid of root mean squared errors, it is concluded that the Denton-Cholette additive method (the first difference) performs better than the Denton-Cholette’s proportional variants as well as Denton’s additive and proportional variants.<b></b></p>

RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland

Quantitative precipitation estimation (QPE) is a difficult task, particularly in complex topography, and requires the adjustment of empirical relations between radar observables and precipitation quantities, as well as methods to transform observations aloft to estimations at the ground level. In this work, we tackle this classical problem with a new twist, by training a random forest (RF) regression to learn a QPE model directly from a large database comprising 4 years of combined gauge and polarimetric radar observations. This algorithm is carefully fine-tuned by optimizing its hyperparameters and then compared with MeteoSwiss' current operational non-polarimetric QPE method. The evaluation shows that the RF algorithm is able to significantly reduce the error and the bias of the predicted precipitation intensities, especially for large and solid or mixed precipitation. In weak precipitation, however, and despite a posteriori bias correction, the RF method has a tendency to overestimate. The trained RF is then adapted to run in a quasi-operational setup providing 5 min QPE estimates on a Cartesian grid, using a simple temporal disaggregation scheme. A series of six case studies reveal that the RF method creates realistic precipitation fields, with no visible radar artifacts, that appear less smooth than the original non-polarimetric QPE and offers an improved performance for five out of six events.

Temporal disaggregation of daily rainfall data in SuDS design: a case study in Tuscany

&lt;p&gt;Sustainable urban Drainage Systems (SuDS), by themselves or combined with grey traditional infrastructures, help to diminish the runoff volume and peak flow, as well as to improve the water quality. Hydrological design of SuDS is usually based on rainfall volumetric percentiles as the number of rainfall events, N&lt;sub&gt;x&lt;/sub&gt;, or the accumulated volume of the rainfall series, V&lt;sub&gt;x&lt;/sub&gt;, to be managed. Sub-index x refers to common qualities used in SuDS design, like 80, 85, 90 and 95%. Usually, only daily rainfall data are available. Nevertheless, due to the characteristics of the urban watershed involved in the SuDS implementation, the quantification of design parameters for these facilities needs sub-hourly rainfall time series. To overcome this issue, a temporal disaggregation methodology was proposed based on the use of a stochastic rainfall generator model (RainSim V3). We analysed the case of Florence University rain gauge (Tuscany, Italy), by collecting 20 years (in the period from 1998 to 2018) of observed data at 15 minutes time step. First, we verified the ability of RainSim model to reproduce observed rainfall patterns at 15 minutes time-step. The parameters of the stochastic model were estimated using observed data with 24 hours time-step. We generated 100 series of 20 years each with a time step of 15 minutes. We accounted two variables to implement the storm events extraction: a) the Minimum Inter-event Time (MIT) between storm events; 2) the storm volume threshold. We obtained a better characterization of the rainfall regime by applying the temporal disaggregation methodology than using daily-observed data. Second, we compared the SuDS design parameters N&lt;sub&gt;x&lt;/sub&gt; and V&lt;sub&gt;x&lt;/sub&gt;, obtained by using the stochastically generated rainfall, the observed daily and 15 minutes data. Moreover, the effect of different MITs and different thresholds on N&lt;sub&gt;x&lt;/sub&gt; and V&lt;sub&gt;x &lt;/sub&gt;were evaluated. In all the cases, results show that N&lt;sub&gt;x&lt;/sub&gt; and V&lt;sub&gt;x&lt;/sub&gt; obtained with the median of the simulated series were closer to the actual observed parameters based on 15 minutes time step than the ones calculated with the observed daily data. Therefore, the proposed temporal disaggregation method arises as an efficient technique to overcome the lack of sub-hourly rainfall data, necessary to adequately design SuDS.&lt;/p&gt;

A Stochastic Procedure for Temporal Disaggregation of Daily Rainfall Data in SuDS Design

Hydrological design of Sustainable urban Drainage Systems (SuDS) is commonly achieved by estimating rainfall volumetric percentiles from daily rainfall series. Nevertheless, urban watersheds demand rainfall data at sub-hourly time step. Temporal disaggregation of daily rainfall records using stochastic methodologies can be applied to improve SuDS design parameters. This paper is aimed to analyze the ability of the synthetic rainfall generation process to reproduce the main characteristics of the observed rainfall and the estimation of the hydrologic parameters often used for SuDS design and by using the generally available daily rainfall data. Other specifics objectives are to analyze the effect of Minimum Inter-event Time (MIT) and storm volume threshold on rainfall volumetric percentiles commonly used in SuDS design. The reliability of the stochastic spatial-temporal model RainSim V.3 to reproduce observed key characteristics of rainfall pattern and volumetric percentiles, was also investigated. Observed and simulated continuous rainfall series with sub-hourly time-step were used to calculate four key characteristics of rainfall and two types of rainfall volumetric percentiles. To separate independent rainstorm events, MIT values of 3, 6, 12, 24, 48 and 72 h and storm volume thresholds of 0.2, 0.5, 1 and 2 mm were considered. Results show that the proposed methodology improves the estimation of the key characteristics of the rainfall events as well as the hydrologic parameters for SuDS design, compared with values directly deduced from the observed rainfall series with daily time-step. Moreover, MITs rainfall volumetric percentiles of total number of rainfall events are very sensitive to MIT and threshold values, while percentiles of total volume of accumulated rainfall series are sensitive only to MIT values.

Stochastorm: A stochastic rainfall simulator for convective storms

Stochastic rainfall generators aim to reproduce the main statistical features of rainfall at small spatial and temporal scales. The simulated synthetic rainfall series are recognized as suitable for use with impact analysis in water, agricultural, and ecological management. Convection-driven precipitation, dominant in certain regions of the world such as the intertropical belt regions, presents properties that require specific consideration when modeling: (i) strong rainfall intermittency, (ii) high variability of intensities within storms, (iii) strong spatiotemporal correlation of intensities, and (iv) marked seasonality of storm properties. In this article, improvements for an existing statistico-dynamic rainfall generator that models convective storms are presented. Notable novelties include (i) the ability to model precipitation event timing, (ii) an improved temporal disaggregation scheme representing the rainfall distribution at sub-event scales, and (iii) using covariates to reflect seasonal changes in precipitation occurrence and marginal distribution parameters. Extreme values are explicitly considered in the distribution of storm event intensities. The simulator is calibrated and validated using 28 years of five-minute precipitation data from the 30 rain gauge AMMA-CATCH network in the Sahelian region of southwest Niger. Both large propagative systems and smaller local convective precipitation are generated. Results show that simulator improvements coherently represent the local climatology. The simulator can generate scenarios for impact studies with accurate representation of convective precipitation characteristics.

RainForest: A random forest algorithm for quantitative precipitation estimation over Swizerland

Quantitative precipitation estimation (QPE) is a difficult task, particularly in complex topography, and requires the adjustment of empirical relations between radar observables and precipitation quantities, as well as methods to transform observations aloft to estimations at the ground level. In this work, we tackle this classical problem with a new twist, by training a random forest (RF) regression to learn a QPE model directly from a large database comprising four years of combined gauge and polarimetric radar observations. This algorithm is carefully fine-tuned by optimizing its hyper-parameters and then compared with MeteoSwiss' current operational non-polarimetric QPE method. The evaluation shows that the RF algorithm is able to significantly reduce the error and the bias of the predicted precipitation intensities, especially for large and solid/mixed precipitation. In weak precipitation, however, and despite a-posteriori bias correction, the RF method has a tendency to overestimate. The trained RF is then adapted to run in a quasi-operational setup providing 5 minute QPE estimates on a Cartesian grid, using a simple temporal disaggregation scheme. A series of six case-studies reveal that the RF method creates realistic precipitation fields, with no visible radar artifacts, that appear less smooth then the original non-polarimetric QPE, and offers an improved performance for five out of six events.

On the Temporal Variability of Air Pollutants’ Emissions – Case Study of Residential PM10 Emission in Silesian Metropolis

The paper summarizes previous studies associated with carrying out of the air pollutant emission inventories. There are presented three approaches for obtaining monthly distribution of PM10 air emission: using expert’s judgement, modelling of the heating demand, and temporal disaggregation using the heating degree days (HDD). However some differences due to not considering hot water demand, it can be effectively used for obtaining temporal, and spatiotemporal distributions of air pollutants’ air emissions necessary for air quality modelling.