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Calibration of the cumulus parameterization scheme for localized areas is one method that can improve numerical weather prediction rainfall forecast accuracy. Calibration for model development, however, is a time-consuming procedure that requires numerous simulations. The utilization of an efficient method for calibrating complex dynamical models can help mitigate the heavy computational costs involved. In this study, five parameters in the Weather Research and Forecasting (WRF) Kain–Fritsch cumulus scheme were calibrated based on rainfall verification in the northwest and the southeast regions of the Philippines. Two optimization methods—Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Surrogate Modeling-Based Optimization (ASMO)—were used to find the best parameter set values. Both methods generated a higher coefficient of downdraft (Pd), lower entrainment (Pe), and longer convective available potential energy (CAPE) consumption time (Pc), which were found to result in better skill scores than the default WRF. Precipitation amount in both calibrated models decreased leading to an overall less wet bias. Precipitation skill score in the northwestern Philippines significantly improved by 35%, while that of the southeastern Philippines only increased by 3%. In addition, model calibration had no significant effect on the simulated temperature and wind speed. The results show that calibration of the cumulus parameterization scheme yields better results for convective rainfall rather than rain from stratiform clouds, which is expected since the cumulus parameterization scheme represents the effects of sub-grid-scale convective processes.

Precipitation is the most important weather parameter in the Philippines. Made up of more than 7100 islands, the Philippine archipelago is an agricultural country that depends on rain-fed crops. Located in the western rim of the North West Pacific Ocean, this tropical island country is very vulnerable to tropical cyclones that lead to severe flooding events. Recently, satellite-based precipitation estimates have improved significantly and can serve as alternatives to ground-based observations. These data can be used to fill data gaps not only for climatic studies, but can also be utilized for disaster risk reduction and management activities. This study characterized the statistical errors of daily precipitation from four satellite-based rainfall products from (1) the Tropical Rainfall Measuring Mission (TRMM), (2) the CPC Morphing technique (CMORPH) of NOAA and (3) the Global Satellite Mapping of Precipitation (GSMAP) and (4) Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks (PERSIANN). Precipitation data were compared to 52 synoptic weather stations located all over the Philippines. Results show GSMAP to have over all lower bias and CMORPH with lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In addition, a dichotomous rainfall test reveals GSMAP and CMORPH have low Proportion Correct (PC) for convective and stratiform rainclouds, respectively. TRMM consistently showed high PC for almost all raincloud types. Moreover, all four satellite precipitation showed high Correct Negatives (CN) values for the north-western part of the country during the North-East monsoon and spring monsoonal transition periods.

Weather forecasts dictate our daily activities and allow us to respond properly during extreme weather events. However, weather forecasts are never perfect, but differences with model output and with observations can be minimized. Discrepancies between meteorological observations and weather model outputs are often caused by resolution differences (point vs. grid comparisons) and by the parameterizations used in the model. Atmospheric model parameterization refers to substituting small-scale and complicated atmospheric processes by simplified ones. In order to make weather forecasts more accurate, one can either increase the model resolution or improve the parameterizations used. Increasing model resolution can simulate small-scale atmospheric processes better, but takes a longer simulation time. On the other hand, improving model parameterization schemes involve in-depth measurements, analysis and research on numerous atmospheric processes. However, one can find a combination of existing parameterization schemes that would minimize observation-model differences. It is therefore essential to ask the question, “What model resolution and parameterization scheme combinations at a particular location and at particular seasons produce model output that has the smallest difference with observations simulated at a reasonable amount of time?” Northern Thailand is a meteorologically active and unstable region especially during the summer and monsoon months (e.g. intense thunderstorms, hail storms, etc). It is also where high concentrations of air pollutants occur during the dry months (e.g. haze). It is therefore essential to have model forecasts close to observations for this region to reduce risk from weather and from air quality degradation. This study aims to find the optimum model resolution and parameterization scheme combinations at particular provinces in northern Thailand with available data during the wet and dry seasons that produces minimum differences with observations. Nested model simulations were performed using the Weather Research and Forecasting (WRF) model (v. 3.6) ran in the High-Performance Computer (HPC) cluster of the National Astronomical Research Institute of Thailand (NARIT) for northern Thailand (2 km spatial resolution and hourly output), for the whole of Thailand (10 km spatial resolution and hourly output), and for the entire Southeast Asia (50 km spatial resolution and 3-hourly output). Combinations of the WRF Single-Moment 3-class, the WRF Single-Moment 5-class, the Lin et al. (Purdue), the WRF Single-Moment 6-class and the WRF Double-Moment 6-class microphysics parameterization schemes, as well as the Betts-Miller-Janjic, the Kain-Fritsch scheme, the Grell-Freitas (GF) ensemble and the Grell 3D cumulus parameterization schemes were utilized to determine the optimum resolution and parameterization of the model when compared to observations. Measured data came from the Thai Meteorological Department (TMD) weather stations in Chiang Mai, Chiang Rai and Lampang in northern Thailand from December 1-15, 2014 (cool dry season), from May 1-12 (hot dry season) and from August 1-7, 2015 (wet season). Results showed a seasonal dependence on the optimum microphysics and convective parameterization combination scheme. It was also found out that cloud resolving model grid sizes still failed to capture convective process as indicated by the derived optimum resolution for the hot-dry and wet seasons.

Wind energy is one of the best options for renewable energy such that, many researchers work on wind resource assessment, specifically using numerical weather prediction (NWP) model to forecast atmospheric behavior on a given domain. In addition, every combination of parameterization configuration influences wind assessment. At the same time, choosing the optimum vertical and horizontal resolution may affect its output and processing time. Regardless of available researches, most of them focuses on mid-latitude area but not in tropical areas like the Philippines. In the study, sensitivity analysis of Weather Research and Forecasting (WRF) model version 3.6.1 with 4 configurations was performed. The duration of the simulation was from January 1, 2014 00:00 to December 31, 2014 23:00. The parameters involved were horizontal resolution and vertical levels. Also, meteorological input data from NCEP Final Analysis with 1 degree resolution every 6 hours was used. For validation, wind speed measurements at 10 m height from NOAA Integrated Surface Database (ISD) were utilized, of which, the 3 weather stations are located in Manila, Science Garden and Ninoy Aquino International Airport (NAIA). The results show that increasing horizontal resolution from 4 km to 1 km have no significant increase to wind speed accuracy. In majority, higher vertical levels tend to increase its accuracy. Moreover, the model has higher accuracy during the rainy season and months of April and May. Overall, the model overestimated the observed wind speed but the diurnal cycle of wind speed follows all the simulation.

The influence of humidity is considered on the concentration of the suspended particulate matter (SPM) measured with a β -ray counter. The humidity condition inside a small observatory where the counter is located is, in general, different from the ambient condition outside the observatory. From the measured values, the ambient SPM concentration is derived considering the hygroscopic effect of common aerosol species of sea salt (SS), (NH 4 ) 2 SO 4 , NH 4 NO 3 and NaNO 3 . In a case study conducted during September 2005, temperature and humidity were measured both inside and outside the observatory. The average value of the relative humidity is 48% for inside and 78% for outside, resulting in approximately 53% larger SPM mass concentration after the correction. Accordingly, the value of mass extinction efficiency, which is given by the ratio between the optically measured extinction coefficient and the mass concentration, becomes lower after the correction.

Lidar data observed by two continuously operated portable automated lidar (PAL) systems and images from the visible and thermal infrared channels of the advanced very high resolution radiometer (AVHRR) sensor on board the noaa16 satellite are employed for the characterization of cloud heights and cloud types. The PAL systems are located in Chiba and Ichihara city areas, separated by approximately 10 km. Measurements from October 2003 to March 2005 reveal that monthly averages of cloud base height and cloud cover ratio show good agreement between the two sites. The characteristics of the vertical (Chiba) and slant (Ichihara) measurements are also discussed. The PAL data are used to adjust threshold values of a cloud-type classification method in split-window data of noaa16-AVHRR. Comparisons between the lidar signals and the cloud classification results from the concurrent AVHRR images show that the classification method can reasonably be applied to this mid-latitude case, although the split-window technique was originally developed for tropical clouds.

The Weather Research and Forecasting (WRF v. 3.7) model was applied to model PM10 data in Chiang Mai city for 10-days during a high haze event utilizing updated land use categories from the Moderate Resolution Imaging Spectroradiometer (MODIS). A higher resolution meteorological lateral boundary condition (from 1 degree to 0.25 degree) was also used from the NCEP GDAS/FNL Global Tropospheric Analyses and Forecast Grid system. A 3-category urban canopy model was also added and the Thompson aerosol-aware microphysics parameterization scheme was used to model the aerosol number concentrations that were later converted to PM10 concentrations. Aerosol number concentration monthly climatology was firstly used as initial and lateral boundary conditions to model PM10 concentrations. These were compared to surface data obtained from two stations of the Pollution Control Department (PCD) of Thailand. The results from the modeled PM10 concentrations could not capture the variability (r = 0.29; 0.27 for each site) and underestimated a high PM10 spike during the period studied. The authors then added satellite data to the aerosol climatology that improved the comparison with observations (r = 0.45; 43). However, both model runs still were not able to capture the high PM10 concentration event. This requires further investigation.

Surface carbon dioxide concentrations were measured using a non-dispersive infrared carbon dioxide sensor at Lampang Rajabhat University from April to May 2013 and at the University of the Philippines-Diliman campus starting September 2013. Factors influencing the variations in these measurements were determined using multiple linear regression and a Lagrangian transport model. Air temperature and sea level pressure were the dominant meteorological factors that affect the CO2 variations. However, these factors are not enough. Surface CO2 flux and transboundary transport needs to be considered as well.

Atmospheric Data Collection Lidar (ADCL) of the Center for Environmental Remote Sensing (CEReS), Chiba University, is a multi-wavelength lidar system designed for measuring tropospheric aerosols and clouds with ancillary data from ground-based aerosol measurement instruments. In this paper, we report on the concept of aerosol and cloud retrieval based on vertical, slant-path, and plan-position indicator (PPI) lidar measurements in combination with aerosol measurements conducted with a three-wavelength integrating nephelometer, an aethalometer, and a particle counter. It is expected that such a combined approach makes it possible to study the detailed features of aerosols in the troposphere, including the aerosol-cloud interaction.

We have developed and operated a portable automated lidar (PAL) system that is capable of unattended, continuous operation under all-weather conditions. Here we report the features of the observed signals that convey the information on the temporal and spatial dynamics of the aerosols and clouds in the troposphere, illustrating the advantage of the continuous operation through nighttime and daytime.