Numerical Forecasts Research Articles

Evaluation of High-Frequency Channels for Deep-Space Data Transmission Using Radiometeorological Model Forecast

The aim of this paper is to investigate the usability of high-frequency channels for deep-space (DS) transmissions exploiting radiometeorological forecast modeling. A previously developed model chain for DS link-budget optimization, based on numerical weather forecasts (WFs), is adopted. The latter, already tested at Ka-band, exploits the combination of a high-resolution mesoscale forecast model and a radiative transfer model to predict the atmospheric scenario and optimize received data volume (DV) during DS transmissions. To shift available Ka-band results to other frequencies, we apply frequency-scaling laws to extrapolate forecast path attenuation, link parameters, and maximum allowed bit-rate for data transmission. Exploiting the available WF-based methodology, we compute DV return for DS missions operating at $X$ -, $K$ -, Ka-, $Q$ -, and $W$ -bands in order to make a comparative study of the behavior of DS transmission-channels at these frequencies. Results show that, in terms of received DV, an innovative WF-based approach is more convenient than traditional methodologies and exhibits a trend similar to the benchmark (ideal case). Increasing link frequency, received DV increases up to $Q$ -band. From $Q$ - to $W$ -band, despite received DV does not increase significantly, lost data remain under reasonable values, thus making the $W$ -band suitable if coupled with a WF-based technique.

Improving NOAA NAQFC PM2.5 Predictions with a Bias Correction Approach

Abstract Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is a critical air pollutant with important impacts on human health. It is essential to provide accurate air quality forecasts to alert people to avoid or reduce exposure to high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance of surface PM2.5 for the United States. However, the NAQFC forecast guidance for PM2.5 has exhibited substantial seasonal biases, with overpredictions in winter and underpredictions in summer. To reduce these biases, an analog ensemble bias correction approach is being integrated into the NAQFC to improve experimental PM2.5 predictions over the contiguous United States. Bias correction configurations with varying lengths of training periods (i.e., the time period over which searches for weather or air quality scenario analogs are made) and differing ensemble member size are evaluated for July, August, September, and November 2015. The analog bias correction approach yields substantial improvement in hourly time series and diurnal variation patterns of PM2.5 predictions as well as forecast skill scores. However, two prominent issues appear when the analog ensemble bias correction is applied to the NAQFC for operational forecast guidance. First, day-to-day variability is reduced after using bias correction. Second, the analog bias correction method can be limited in improving PM2.5 predictions for extreme events such as Fourth of July Independence Day firework emissions and wildfire smoke events. The use of additional predictors and longer training periods for analog searches is recommended for future studies.

Improving Wind-Ramp Forecasts in the Stable Boundary Layer

The viability of wind-energy generation is dependent on highly accurate numerical wind forecasts, which are impeded by inaccuracies in model representation of boundary-layer processes. This study revisits the basic theory of the Mellor, Yamada, Nakanishi, and Niino (MYNN) planetary boundary-layer parametrization scheme, focusing on the onset of wind-ramp events related to nocturnal low-level jets. Modifications to the MYNN scheme include: (1) calculation of new closure parameters that determine the relative effects of turbulent energy production, dissipation, and redistribution; (2) enhanced mixing in the stable boundary layer when the mean wind speed exceeds a specified threshold; (3) explicit accounting of turbulent potential energy in the energy budget. A mesoscale model is used to generate short-term (24 h) wind forecasts for a set of 15 cases from both the U.S.A. and Germany. Results show that the new set of closure parameters provides a marked forecast improvement only when used in conjunction with the new mixing length formulation and only for cases that are originally under- or over-forecast (10 of the 15 cases). For these cases, the mean absolute error (MAE) of wind forecasts at turbine-hub height is reduced on average by 17%. A reduction in MAE values on average by 26% is realized for these same cases when accounting for the turbulent potential energy together with the new mixing length. This last method results in an average reduction by at least 13% in MAE values across all 15 cases.

Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG

This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Nino–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection- based four-dimensional variational data assimilation (DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors (NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.

MODELLING THE PHOTOVOLTAIC OUTPUT POWER USING THE DIFFERENTIAL POLYNOMIAL NETWORK AND EVOLUTIONARY FUZZY RULES

The unstable production of renewable energy sources, which is difficult to model using conventional computational techniques, may be predicted to advantage by means of biologically inspired soft-computing methods. The photovoltaic output power is primarily dependent on the solar direct or global radiation, which short-term numerical forecasts are possible to apply for daily power predictions. The study compares two methods, which can successfully model dynamic fluctuant variances of the solar irradiance and corresponding output power time-series. Differential polynomial network is a new neural network class, which defines and substitutes for the general partial differential equation to model an unknown system function. Its total output is composed from selected neurons, i.e. relative polynomial substitution terms, formed in all network layers of a multi-layer structure. The proposed derivative polynomial regression using relative dimensionless fraction units, formed according to the Similarity analysis, can describe and generalize data relations on a wider range of values than defined by the training interval when using standard soft-computing composing techniques that apply only absolute data. 1-variable time-series observations are possible to model by time derivatives of a converted ordinary differential equation, solved analogously with partial derivative substitution terms of several time-point variables.

多元信息耦合的致灾山洪降雨预报方法

雷达预估信息(0~2 h)、数值天气预报产品(0~72 h)及基于高空和地面大气探测资料的综合预报信息(0~24 h)在不同预见期的降雨预报各有差异，预报效果上各有优劣，而针对山洪的预报存在准确率低、预警有效时间短等问题，研究面向山洪灾害防治区的数值预报模式的选取、模式最优物理参数化组合方案、同化方案、降水偏差订正技术以及基于大数据的自分型雷达估测降水最优化算法。以湖南临湘市为试验区进行验证，结果表明：WRF中尺度数值模式适用于山洪灾害降雨预报，其WSM6云微物理过程、Grell-Devenyi ensemble对流参数化方案和YSU边界层参数方案对致洪山洪暴雨过程模拟较好，基于频率(或面积)匹配的降水偏差订正方法能显著改善模式降水预报中雨量和雨区范围的系统性偏差，大数据分析方法应用于雷达估测降雨能显著提高准确度。在此研究基础上，提出了基于高空和地面大气探测、数值预报模式的0~72 h短期定量降雨预报和基于雷达的0~2 h临近定量降雨预报的多元信息耦合预报方法，将对致灾山洪的预报时间由2 h延长至72 h，并可显著提高了山洪预报的精度。 The precipitation forecasts from radar and satellite cloud pictures (with a lead time of 0 - 2 h), from upper air and ground surface atmospheric sounding (with a lead time of 0 - 24 h) and from numerical forecasting mode (with a lead time of 0 - 72 h) have different forecast effect in different forecast pe-riods. Considering the short lead time and low accuracy forecast for early warning of mountain tor-rents, study on the numerical forecasting model, the model’s optimized combination of parameters, data assimilation, correcting the errors on forecasted rainfall in flash-flood affected region and an op-timized calculating method based on big data for radar monitoring precipitation. Linxiang City of Hu-nan Province was selected as experimental area and the results show that the WRF numerical forecast is more suitable for the forecast of disasters including mountain torrents. Among them, WSM6 cloud microphysical process, Grell-Devenyi ensemble convective parameterization scheme and YSU boun-dary layer parameter configuration scheme can greatly simulate the process of mountain torrents and storm causing a flood, precipitation deviation correction method based on frequency (or area) match can considerably improve the systematic deviation in the rainfall and scope of rain area in the preci-pitation forecast, the big data analysis method used in radar rainfall forecast can remarkably improve the accuracy. A forecasting disastrous flash floods method has been proposed by coupling upper air and ground surface atmospheric sounding, the 0 - 72 h short-term quantitative precipitation forecasts (QPF) from numerical forecasting mode and the 0 - 2 h nowcast QPF based on radar, which helps to increase the lead time of flash flooding forecasts from 2 h to 72 h and enhance significantly the fore-casting accuracy.

Study on Precipitation Forecast and Testing Methods of Numerical Forecast in Fuxin Area

This research uses Japan facsimile data in May-September of 2011-2015 in Fumeng and Zhangwu County of Fuxin, Liaoning, China as the site selection for high altitude NECV circulation background, using statistical and synoptic methods testing the combination of test and Analysis on Japanese numerical precipitation prediction and error. The prediction equation of cold vortex precipitation in the region is established by SPSS software. It is predicted whether the product predicts precipitation, water vapor condition and vertical velocity according to the Japanese numerical forecast. The results show that the fitting rate is 88% in the past 5 years, and the rate of cold vortex precipitation in the summer of 2016 is 89%.

Japanese space weather research activities

AbstractIn this paper, we present existing and planned Japanese space weather research activities. The program consists of several core elements, including a space weather prediction system using numerical forecasts, a large‐scale ground‐based observation network, and the cooperative framework “Project for Solar‐Terrestrial Environment Prediction (PSTEP)” based on a Grant‐in Aid for Scientific Research on Innovative Areas.

Spatial ensemble post‐processing with standardized anomalies

To post‐process ensemble predictions for a particular location, statistical methods are often used, especially in complex terrain such as the Alps. When expanded to several stations, the post‐processing has to be repeated at every station individually, thus losing information about spatial coherence and increasing computational cost. Therefore, the ensemble post‐processing is modified and applied simultaneously at multiple locations. We transform observations and predictions to standardized anomalies. Seasonal and site‐specific characteristics are eliminated by subtracting a climatological mean and dividing by the climatological standard deviation from both observations and numerical forecasts. This method allows us to forecast even at locations where no observations are available. The skill of these forecasts is comparable to forecasts post‐processed individually at every station and is even better on average.

A Novel Methodology of Stochastic Short Term Forecasting of Cloud Boundaries

Following the chaos theory proposed by Lorenz, probabilistic approaches have been widely used in numerical weather prediction. This paper introduces a convection diffusion equation to quantify the dynamic growth of uncertainty for short term forecasts of cloud boundaries. The equation is inserted into a numerical weather prediction model, weather research and forecast. A two parameter model based on wind velocity dispersion and surface evaporation rates parameterizes the stochastically motivated, but deterministic, equation. Prediction verification tests in comparison to observed data show good predictive capability for an hour, with a gradual loss of predictive power continuing for predictions up to three hours shown qualitatively. The methodology can be applied to a variety of topics in numerical weather prediction research.

DYNAMIC ERROR OF THE TEMPERATURE SENSORS WITH THE SOUNDING OF THE ATMOSPHERE

Radiosounding of the atmosphere is an essential component constituting the base for prognostic aviation authorities. GPR scanning data are the basis for the mapping of baric topography used in the development of aviation weather forecasts. Currently, numerical methods of weather forecast have become especially popular. This is quite justified, as these methods allow to increase the accuracy of weather forecasts and these techniques represent the future. However, the era of "numerical weather prediction" will not come soon. This is primarily due to imperfect numerical forecast models, which do not provide for timeliness and reliability of weather forecasting required for aviation. However, the quality of meteorological support of aircraft flights is largely determined by the timeliness and predictability of the aviation weather forecasts. In this regard, the network of radiosounding functions require the presentation of the theoretical foundations and providing consumers with normalized metrological characteristics of the measuring system of radio sounding, methods of measurement and a reasonable assessment of the reliability of the results of sensing. A number of these problems have been resolved nowadays, however, so far the problem of estimating dynamic measurement errors in the sounding is not solved. The metrological characteristics and the dynamic error of measurement of temperature with the new temperature sensors of foreign production (NТС MFB-5000-3220), recently used in the Russian radiosondes still require the detailed studies. This article is devoted to one of the most important types of errors of radio sounding – the dynamic errors of measurement, to be precise, the dynamic error of the temperature measurement. In the article the problem of determining the value of dynamic errors of radiosondes is being solved alongside with the investigation of the role of this kind of errors when assessing the reliability of the results of the atmosphere radio sounding.

Ice Fog: The Current State of Knowledge and Future Challenges

Abstract Ice fog is a natural, outdoor cloud laboratory that provides an excellent opportunity to study ice microphysical processes. Ice crystals in fog are formed through similar pathways as those in elevated clouds; that is, cloud condensation or ice nuclei are activated in an atmosphere supersaturated with respect to liquid water or ice. The primary differences between surface and elevated ice clouds are related to the sources of water vapor, the cooling mechanisms and dynamical processes leading to supersaturation, and the microphysical characteristics of the nuclei that affect ice fog crystal physical properties. As with any fog, its presence can be a hazard for ground or airborne traffic because of poor visibility and icing. In addition, ice fog plays a role in climate change by modulating the heat and moisture budgets. Ice fog wintertime occurrence in many parts of the world can have a significant impact on the environment. Global climate models need to accurately account for the temporal and spatial microphysical and optical properties of ice fog, as do weather forecast models. The primary handicap is the lack of adequate information on nucleation processes and microphysical algorithms that accurately represent glaciation of supercooled water fog. This chapter summarizes the current understanding of ice fog formation and evolution; discusses operating principles, limitations, and uncertainties associated with the instruments used to measure ice fog microphysical properties; describes the prediction of ice fog by the numerical forecast models and physical parameterizations used in climate models; identifies the outstanding questions to be resolved; and lists recommended actions to address and solve these questions.

Verification of Mongolian Cyclone-Induced Snowstorm Model Forecast in Jilin Province, China

Situation field forecast and rainfall forecast in typical numerical forecast models including EC (The European Centre for Medium-Range Weather Forecasts), t639 (T639 Global Forecast System) and Japanese model were verified by set statistics and TS (Threat Score) scoring based on 8 cases of Mongolian cyclone-induced snowstorm in Jilin Province in this paper. As shown by the results, for the forecast of Mongolian cyclone location and intensity, EC has significantly higher accuracy than Japanese model and t639, and there is a high likelihood that it forecasts the southerly cyclone location, relatively fast movement and comparatively weak intensity within 72 hours; for snowfall forecast, Japanese model shows significantly higher accuracy than other models, especially it has obviously stronger ability to forecast the heavy rainfall above snowstorm than other models, while WRF model (The Weather Research and Forecasting Model) has strong forecast ability of normal snowfall; for normal snowfall, the 72-hour missing forecast rate is higher than false forecast rate in all the models.

Using the Characteristic Equation to Estimate the Initial Values for Numerical Forecasts

Japan is an island nation that experiences frequent earthquakes. When an earthquake occurs, it is important to forecast its resultant tsunami: its size, location, time of arrival, etc. These forecasts are made using numerical simulations. The initial conditions are very important for numerical simulations, but the small number of tide stations makes it difficult to make highly precise forecasts. The distance between stations is normally several tens of km, and this lowers the precision of the initial data afforded by them. It is therefore common to use data interpolated from the sparse observation data at timet=0. Even so, high-resolution interpolation cannot be expected since the original data is of poor quality. In addition, the interpolated values may not be physically valid because the governing equation may not have been considered when the data were interpolated. We therefore propose a new method of estimating the initial value by using a characteristic equation. In this method, we replace the spatial resolution with time resolution. This results in a high-resolution initial value because the same place is measured more than once. In addition, the characteristic equation is based on the governing equation. Therefore, in this method, an accurate estimation of initial value is considered to be possible. In this paper, we show two applications of this approach, one for a dimensional shallow water wave equation and one for Euler’s equation. The shallow water wave equation is for the tsunami, and the Euler equation is the governing equation of the numerical weather forecast.

Forecast Transparency and Inflation Volatility: Empirical Evidence from Developing Countries

The desirability of transparency in its varied aspects is not always certain particularly for developing countries. This paper empirically investigates the link between forecast transparency and inflation volatility in a relatively homogenous sample of 41 developing economies over 1998–2007. It classifies central banks into transparent and opaque groups on the basis of their forecast transparency. The differential effect of the forecast transparency is then estimated both at a point in time and over time while controlling for the relevant factors. The main finding of the paper is that forecast transparency stabilizes inflation. In addition, we find that the stabilizing effect of disclosing numerical forecasts gets stronger over time supporting the theoretical insight that forecast transparency allows reputational externalities, and minimizes the likelihood of policy inconsistency. The results are robust against different specifications of the empirical model.

Impacts of aerosol\u2013monsoon interaction on rainfall and circulation over Northern India and the Himalaya Foothills

The boreal summer of 2008 was unusual for the Indian monsoon, featuring exceptional heavy loading of dust aerosols over the Arabian Sea and northern-central India, near normal all-India rainfall, but excessive heavy rain, causing disastrous flooding in the Northern Indian Himalaya Foothills (NIHF) regions, accompanied by persistent drought conditions in central and southern India. Using the NASA Unified-physics Weather Research Forecast (NUWRF) model with fully interactive aerosol physics and dynamics, we carried out three sets of 7-day ensemble model forecast experiments: (1) control with no aerosol, (2) aerosol radiative effect only and (3) aerosol radiative and aerosol-cloud-microphysics effects, to study the impacts of aerosol-monsoon interactions on monsoon variability over the NIHF during the summer of 2008. Results show that aerosol-radiation interaction (ARI), i.e., dust aerosol transport, and dynamical feedback processes induced by aerosol-radiative heating, plays a key role in altering the large-scale monsoon circulation system, reflected by an increased north-south tropospheric temperature gradient, a northward shift of heavy monsoon rainfall, advancing the monsoon onset by 1–5 days over the HF, consistent with the EHP hypothesis (Lau et al. in Clim Dyn 26(7–8):855–864, 2006). Additionally, we found that dust aerosols, via the semi-direct effect, increase atmospheric stability, and cause the dissipation of a developing monsoon onset cyclone over northeastern India/northern Bay of Bengal. Eventually, in a matter of several days, ARI transforms the developing monsoon cyclone into meso-scale convective cells along the HF slopes. Aerosol-Cloud-microphysics Interaction (ACI) further enhances the ARI effect in invigorating the deep convection cells and speeding up the transformation processes. Results indicate that even in short-term (up to weekly) numerical forecasting of monsoon circulation and rainfall, effects of aerosol-monsoon interaction can be substantial and cannot be ignored.

Parallelism and optimization of numerical ocean forecasting model

According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China (MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation (SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface (MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation (172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products.

Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

The numerical forecast of particulate matter concentrations in general, and PM10 in particular is a theme of high socio-economic relevance. The aim of this study was to investigate the impact of ground and satellite data assimilation of PM10 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) numerical air quality model for Romanian territory. This is the first initiative of the kind for this domain of interest. Assimilation of satellite information – e.g. AOT's in air quality models is of interest due to the vast spatial coverage of the observations. Support Vector Regression (SVR) techniques are used to estimate the PM content from heterogeneous data sources, including EO products (Aerosol Optical Thickness), ground measurements and numerical model data (temperature, humidity, wind, etc.). In this study we describe the modeling framework employed and present the evaluation of the impact from the data assimilation of PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilation enabled/disabled configurations allowed the evaluation of satellite and ground data assimilation impact on the PM10 forecast performance for the Romanian territory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013).

The Warm-Core Structure of Hurricane Earl (2010)

Abstract The warm-core structure of Hurricane Earl (2010) is examined on four different days, spanning periods of both rapid intensification (RI) and weakening, using high-altitude dropsondes from both the inner core and the environment, as well as a convection-permitting numerical forecast. During RI, strong warming occurred at all heights, while during rapid weakening, little temperature change was observed, implying the likelihood of substantial (unobserved) cooling above flight level (12 km). Using a local environmental reference state yields a perturbation temperature profile with two distinct maxima of approximately equal magnitude: one at 4–6-km and the other at 9–12-km height. However, using a climatological-mean sounding instead results in the upper-level maximum being substantially stronger than the midlevel maximum. This difference results from the fact that the local environment of Earl was warmer than the climatological mean and that this relative warmth increased with height. There is no obvious systematic relationship between the height of the warm core and either intensity or intensity change for either reference state. The structure of the warm core simulated by the convection-permitting forecast compares well with the observations for the periods encompassing RI. Later, an eyewall replacement cycle went unforecast, and increased errors in the warm-core structure are likely related to errors in the forecast wind structure. At most times, the simulated radius of maximum winds (RMW) had too great of an outward slope (the upper-level RMW was too large), and this is likely also associated with structural biases in the warm core.

Meteorological Elements Used in the Numerical Forecast of PM10 Over the Romanian Territory

Abstract Pollutants in large amounts may not only cause severe health problems and damage crops, but may also represent one of the main causes of global warming. Air quality numerical models are very useful tools in forecasting air pollutants transport and spread. Consequently, this paper analyzes the regional concentration of PM10 (a geographical domain representative of Romania’s territory), by using the numerical air quality forecasting model: WRF-CHEM version 3.5. The respective model has been applied on a Romania-centered upon geographical area, for both the two months representative of the warm and cold seasons (June and January 2013, respectively), at a spatial resolution of 10 km, with a 24 hours’ anticipation. For the WRF-CHEM chemistry module to be initiated, values of PM10 emissions were extracted from the TNO (Nederlandse Organisatie voor toegepast natuurwetenschappelijk onderzoek, www.tno.nl) database for 2009. The meteorological parameters required by the weather forecast model were obtained from the numerical output of the ECMWF (www.ecmwf.int) global model. All these meteorological parameters are very important for air quality simulations, especially wind profiles, which are very important assessment tools since they determine where pollutants are transported, and air-temperature as it may largely influence the speed of chemical reactions in the atmosphere. The vertical diffusion is strongly linked to the height of the mixing layer and influences the exchange between the ground layers and the open troposphere. In order to highlight the quality of the numerical forecasts for PM10 from the WRF-CHEM model, the results obtained were compared to the measurements obtained through gravimetric methods (average daily values of PM10). Hence, specific measurements were provided by the National Environmental Protection Agency (ANPM).