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Typhoon (TY) Haiyan was one of the most intense and highly destructive tropical cyclones (TCs) to affect the Philippines. As such, it is regarded as a baseline for extreme TC hazards. Improving the simulation of such TCs will not only improve the forecasting of intense TCs but will also be essential in understanding the potential sensitivity of future intense TCs with climate change. In this study, we investigate the effects of model configuration in simulating TY Haiyan using the Weather Research Forecasting (WRF) Model. Sensitivity experiments were conducted by systematically altering the choice of cumulus schemes, surface flux options, and spectral nudging. In addition to using the European Centre for Medium-Range Weather Forecasts Reanalysis fifth-generation (ERA5) single high-resolution realization as initial and boundary conditions, we also used 4 of the 10 lower-resolution ERA5 data assimilation system (EDA) ensemble members as initial and boundary conditions. Results indicate a high level of sensitivity to cumulus schemes, with a trade-off between using Kain–Fritsch and Tiedtke schemes that have not been mentioned in past studies of TCs in the Philippines. The Tiedtke scheme simulates the track better (with a lower mean direct positional error, DPE, of 33 km), while the Kain–Fritsch scheme produces stronger intensities (by 15 hPa minimum sea level pressure). Spectral nudging also resulted in a reduction in the mean DPE by 20 km, and varying the surface flux options resulted in the improvement of the simulated maximum sustained winds by up to 10 m s−1. Simulations using the EDA members initial and boundary conditions revealed low sensitivity to the initial and boundary conditions, having less spread than the simulations using different parameterization schemes. We highlight the advantage of using an ensemble of cumulus parameterizations to take into account the uncertainty in the track and intensity of simulating intense tropical cyclones.

The potential changes in the characteristics and damage potential of three of the most damaging tropical cyclone (TC) events (Haiyan 2013, Bopha 2012, Mangkhut 2018) in the Philippines have been simulated using the pseudo global warming (PGW) technique. Simulations were performed using the Weather Research and Forecasting model at 5 km resolution with cumulus parameterization (5 kmCU) and 3 km without cumulus parameterization (3 kmNoCU), with PGW deltas derived from a selection of the CMIP6 models. We found that re-forecasting the three TCs under future warming leads to more intense TCs, with changes in maximum wind of 4%, 3%, and 14% for the 5 kmCU runs, and 14%, 4%, and 12% for the 3 kmNoCU runs of Typhoon Haiyan, Bopha, and Mangkhut, respectively. The changes in track, translation speed, and size are relatively small. The TC cases have a higher impact potential in the future, as expressed by the cyclone damage potential index, ranging from ~ 1% to up to 37% under the SSP5-8.5 scenario. Based on the pre-industrial runs, climate change has had, so far, only a weak influence on TC intensity and not much influence on track, translation speed, and size. Simulations without convective parameterization show similar changes in the sign of the projected TC intensity response, but different signals of change in translation speed and size.

The Philippines is exposed to Tropical Cyclones (TCs) throughout the year due to its location in the western North Pacific. While these TCs provide much-needed precipitation for the country’s hydrological cycle, extreme precipitation from TCs may also cause damaging hazards such as floods and landslides. This study examines the relationship between TC extreme precipitation and TC characteristics, including movement speed, intensity, and season, for westward-moving TCs crossing Luzon, northern Philippines. We measure extreme precipitation by the Weighted Precipitation Exceedance (WPE), calculated against a 95th percentile threshold, which considers both the magnitude and spatial extent of TC-related extreme precipitation.WPE has a significant, moderate positive relationship with TC intensity and a significant, weak negative relationship with TC movement speed. When TCs are classified by pre-landfall intensity, Typhoons (1-minute maximum sustained wind speed > 64 knots) tend to yield higher WPE than non-Typhoons (< 64 knots). On the other hand, when TCs are classified by pre-landfall speed, Slow TCs (movement speed < 11.38 knots) tend to yield higher WPE than Fast TCs (movement speed > 11.38 knots). However, while distributions of WPE are similar between the Southwest Monsoon (June-September) and Northeast Monsoon (October-December) seasons, the relationship between pre-landfall TC intensity and WPE is more pronounced during June-September. These results suggest that it is important to consider the pre-landfall cyclone movement speed, intensity, and season to anticipate extreme precipitation of incoming TCs. A decision table considering these factors is devised to aid in TC extreme precipitation forecasting.

Forecasting flooding hazards is a very effective non-engineering measure for flood control. Presently, the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model. Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones, resulting in frequent heavy rainfall events. However, most rivers in the many basins lack water flow observation data, which makes it challenging to use lumped and data-driven models for flash flood forecasting. With the continuous progress of remote sensing (RS) and geographic information system (GIS) techniques, the physically-based distributed hydrological model (PBDHMs) has rapidly advanced. PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data, which makes it possible for flood forecasting in areas with little to virtually no data. In this study, the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region. The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events. The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges. The results conform to the hydrological regularities, which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.

The Philippines is frequently affected by tropical cyclones (TCs), and understanding the flood response of the Pampanga River basin (PRB) from TC-induced rain is needed in effective disaster risk management. As large uncertainties remain in TC rain forecasting, we propose a simple checklist method for flood forecasting of the PRB that depends on the general TC track, season, and accumulated rainfall. To this end, flood events were selected based on the alert, alarm, and critical river height levels established by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Results show that all flood events in the PRB were induced by TCs. All intense TCs that directly traversed the PRB resulted in critical-level floods. These TCs also had the shortest flood onset of 7–27 h from alert to critical level. Flooding from distant landfalling TCs, on the other hand, are dependent on season. TCs traversing north (south) of the PRB induced flooding only during the southwest (northeast) monsoon season. These TCs can raise water levels from alert to critical in 11–48 h. Remote precipitation from non-landfalling TCs can also induce critical-level flooding but with a longer onset time of 59 h. These results indicate that a simple checklist method can serve as a useful tool for flood forecasting in regions with limited data and forecasting resources.

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.

In this study, we examine the effect of the Cordillera Mountain Range (CMR) in Luzon, Philippines on Tropical Cyclone (TC) precipitation. Using the Weather Research and Forecasting model, we simulated multiple TC events with three different terrain profiles: control, reduced CMR, and enhanced CMR. We find that for most of the TC cases overland precipitation increases as mountain height increases. To further understand the interaction between TC precipitation and the mountain range, we examine the effects of relevant dynamical fields, including mountain slope, incoming perpendicular wind speed, and the moist Froude Number (Fw). We highlight that TC precipitation is strongly and positively correlated with the product of approaching wind speeds and mountain slope. It is hypothesized that stronger winds along steeper mountain slopes translate to vertical motion which in turn causes higher amounts of precipitation, especially during TC events. In contrast, the linear relationships with other variables are less clear. It is also worth noting that a significant weakening of TCs may cause less rainfall overland, which is an indirect effect of the mountain range on TC precipitation. Understanding the interactions between TCs and mountain ranges may help in regional quantitative precipitation forecasting efforts in the mountainous regions of the Philippines.

The present study investigates the influence of the Madden–Julian oscillation (MJO) on the intraseasonal variability of summer and winter monsoon rainfall in the Philippines. The monsoon wind–terrain interaction that gives rise to rainfall in the Philippines leads to a sharp contrast between west and east coast rainfall patterns during summer and winter monsoon seasons, respectively. During the summer monsoon season, the eastward-propagating MJO modulates tropical cyclone (TC) activity in the western North Pacific (WNP) on MJO phases 5–7. TC rainfall and remote TC precipitation due to Rossby wave response to TC heating induces strong southwesterly flow that enhances moisture transport and brings anomalous rainfall to the western region of the Philippines. On the other hand, during the winter season, an induced Matsuno–Gill-type pattern to the southeast of the Philippines influences the strength of the Asian winter monsoon. Active (inactive) convection in the tropical Maritime Continent and western Pacific during MJO phases 4–6 produces the anomalous Philippine Sea cyclonic (anticyclonic) circulation. The cyclonic (anticyclonic) anomaly leads to an enhanced (weakened) northeast monsoon flow and wet (dry) rainfall anomaly along the eastern Philippines.

Column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured by a solar viewing portable Fourier transform spectrometer (FTS, EM27/SUN) have been characterized and validated by comparison using in situ profile measurements made during the transfer flights of two aircraft campaigns: Korea-United States Air Quality Study (KORUS-AQ) and Effect of Megacities on the Transport and Transformation of Pollutants at Regional and Global Scales (EMeRGe). The aircraft flew over two Total Carbon Column Observing Network (TCCON) sites: Rikubetsu, Japan (43.46∘ N, 143.77∘ E), for the KORUS-AQ campaign and Burgos, Philippines (18.53∘ N, 120.65∘ E), for the EMeRGe campaign. The EM27/SUN was deployed at the corresponding TCCON sites during the overflights. The mole fraction profiles obtained by the aircraft over Rikubetsu differed between the ascending and the descending flights above approximately 8 km for both CO2 and CH4. Because the spatial pattern of tropopause heights based on potential vorticity values from the ERA5 reanalysis shows that the tropopause height over the Rikubetsu site was consistent with the descending profile, we used only the descending profile to compare with the EM27/SUN data. Both the XCO2 and XCH4 derived from the descending profiles over Burgos were lower than those from the ascending profiles. Output from the Weather Research and Forecasting Model indicates that higher CO2 for the ascending profile originated in central Luzon, an industrialized and densely populated region about 400 km south of the Burgos TCCON site. Air masses observed with the EM27/SUN overlap better with those from the descending aircraft profiles than those from the ascending aircraft profiles with respect to their properties such as origin and atmospheric residence times. Consequently, the descending aircraft profiles were used for the comparison with the EM27/SUN data. The EM27/SUN XCO2 and XCH4 data were derived by using the GGG2014 software without applying air-mass-independent correction factors (AICFs). The comparison of the EM27/SUN observations with the aircraft data revealed that, on average, the EM27/SUN XCO2 data were biased low by 1.22 % and the EM27/SUN XCH4 data were biased low by 1.71 %. The resulting AICFs of 0.9878 for XCO2 and 0.9829 for XCH4 were obtained for the EM27/SUN. Applying AICFs being utilized for the TCCON data (0.9898 for XCO2 and 0.9765 for XCH4) to the EM27/SUN data induces an underestimate for XCO2 and an overestimate for XCH4.

The chemical characteristics of fine particulates in Metropolitan Manila, capital of the Philippines, were investigated. Week-long daily PM2.5 sampling observations were done in the months of May and October of 2018 in the Manila Bay area. May and October correspond to the boreal spring and fall monsoon transition periods, respectively. The sampling periods were selected to minimize the contribution of transboundary pollutants and to determine the influence of local mesoscale weather under similar synoptic flow conditions. Results show the daily average PM2.5 mass concentration for spring and fall to be 29.07 ± 4.69 and 50.06 ± 8.79 μg m − 3 , respectively. Most PM2.5 components were also found to be higher during fall. The difference in concentration between the two seasons is mainly due to the strong land/sea-breeze in spring induced by warmer land surface temperature. In spring, strong sea-breeze disperses PM2.5 inland, and the nighttime land-breeze serves as the outflow path of fine aerosols from Metropolitan Manila. In fall, the opposing flow of the weaker sea-breeze to the prevailing easterly produces stagnant air that leads to higher accumulated particulate concentration. Moreover, the suppressed outflow during fall season resulted in higher PM2.5 contribution of aged secondary aerosol components. Overall, the largest contributors to Metropolitan Manila PM2.5 mass concentration are the primary and secondary aerosols from local vehicular emissions, followed by soil dust and seasalt.

This study investigated the inter-correlation of atmospheric PM2.5 between southern Taiwan and the northern Philippines. 24-hour samples of PM2.5 were simultaneously collected at two remote sites, Checheng (Taiwan) and Laoag (Philippines), during all four seasons. The water-soluble ions, metallic elements, carbonaceous content, and anhydrosugars in the PM2.5 were then analyzed to characterize the chemical fingerprint. Furthermore, principal component analysis, chemical mass balance (CMB) receptor modeling, and backward trajectory simulation were applied to resolve the source apportionment of PM2.5 at both of the sites. The results showed that Checheng and Laoag were highly influenced by polluted air masses transported long-range from the north, producing elevated PM2.5 concentrations during winter and spring. The water-soluble ions (WSIs) were abundant in secondary inorganic aerosols (SO42–, NO3–, and NH4+), which accounted for 34.1–76.0%. Crustal elements dominated the metallic content in the PM2.5, but the concentrations of trace elements originating from anthropogenic sources increased during the northwestern monsoon periods. More organic carbon (OC) than elemental carbon (EC) was found, with secondary OC (SOC) contributing approximately 23.9–38.9% to the former. Moreover, the level of levoglucosan highly correlated with those of K+ and OC, confirming that these three substances are key indicators of biomass burning. The two sites exhibited similar chemical compositions for PM2.5, indicating the possibility of transport between Checheng and Laoag, and a paired t-test obtained a p-value of 0.030 (p < 0.05), implying a potential inter-correlation for PM2.5 between southern Taiwan and the northern Philippines. The major sources of the PM2.5 were soil dust, mobile sources, sea salt, and biomass burning along the northerly transport routes during winter and spring. The contribution of anthropogenic sources (i.e., industrial boilers, waste incinerators, and secondary aerosols) to the PM2.5 frequently increased during winter and spring, unlike during summer, suggesting that the northerly transport of PM2.5 highly affected particulate air pollution at both of the sites.

In this study, we used ground instruments, namely, a visibility meter, an integrating nephelometer, an aethalometer, a lidar, and a weather monitor, to measure the scattering enhancement factor, f(RH), which quantifies the effect of ambient relative humidity (RH) on aerosol light-scattering, and to generate a model of its annual variation in the city of Chiba, Japan. First, the f(RH) values were calculated with chemical analysis data. Second, visibility-meter and aethalometer data were used to model the monthly trends of f(RH) at 550 nm. The f(RH) values were higher during summer than during the other three seasons, which can be attributed to the general pattern of the regional climatology as well as the loading of different particle types into the lower troposphere. Third, the f(RH) values at 532 nm were obtained from lidar and aethalometer measurements. Low and constant f(RH) values were observed during November, whereas higher and increasing f(RH) values were observed during May. Also, dust events during March 2015 showed decreasing f(RH) with increasing RH .

This study inter-compared the concentration and chemical characteristics of PM

Column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured by a solar viewing portable Fourier transform spectrometer (FTS, EM27/SUN) have been characterized and validated by comparison using in-situ profile measurements made during the transfer flights of two aircraft campaigns: Korea-United States Air Quality Study (KORUS-AQ) and Effect of Megacities on the Transport and Transformation of Pollutants on the Regional and Global Scale (EMeRGe). The aircraft flew over two Total Carbon Column Observing Network (TCCON) sites: Rikubetsu, Japan (43.46° N, 143.77° E) for the KORUS-AQ campaign and Burgos, Philippines (18.53° N, 120.65° E) for the EMeRGe campaign. The EM27/SUN was deployed at the corresponding TCCON sites during the overflights. The mole fraction profiles obtained by the aircraft over Rikubetsu differed between the ascending and the descending flights above approximately 8 km for both CO2 and CH4. Because the spatial pattern of tropopause heights based on potential vorticity values from the ERA5 reanalysis show that the tropopause height over the Rikubetsu site was consistent with the descending profile, we used only the descending profile to compare with the EM27/SUN data. Both the XCO2 and XCH4 derived from the descending profiles over Burgos were lower than those from the ascending profiles. Output from the Weather Research and Forecast Model indicate that higher CO2 for the ascending profile originated in central Luzon, an industrialized and densely populated region about 400 km south of the Burgos TCCON site. Air masses observed with the EM27/SUN overlap better with those from the descending aircraft profiles than those from the ascending aircraft profiles with respect to their properties such as origin and atmospheric residence times. Consequently, the descending aircraft profiles were used for the comparison with the EM27/SUN data. The EM27/SUN XCO2 and XCH4 data were derived by using the GGG2014 software in which air mass independent correction factors utilized for the TCCON data (0.9898 for XCO2 and 0.9765 for XCH4) were not applied. The comparison of the EM27/SUN observations with the aircraft data revealed that on average, the EM27/SUN XCO2 data were biased low by 1.22 % and the EM27/SUN XCH4 data were biased low by 1.67 %. The resulting air mass independent correction factors of 0.9878 for XCO2 and 0.9833 for XCH4 were obtained for the portable FTS.

Metro Manila, Philippines and other urban areas have reached internationally known unacceptable levels of pollution where about 80% can be attributed to vehicular emissions. The Weather Research and Forecasting model coupled with Chemistry v.3.6.1 was used in the reanalysis of pollutant concentrations for the year 2013. Initial results from the planetary boundary layer study suggested that the Yonsei University scheme provides a good estimate of the atmosphere’s condition; hence, this setting was used for the succeeding simulations. The land coverage over Sangley point was not properly resolved by the model. This caused a cold bias for the station. Further evaluation of the model’s sea level pressure output for all sites returned high correlations showing that modeled values are in phase with the observed time series; however, wind speed values did not correlate well with the observed values and were all overestimated. The low correlations found were a result of the incapability of the model to detect the urban canopy layer over Metro Manila. Pollutant concentrations were overestimated. The pollutant time series suggests that the model overestimates concentration values for PM10, PM2.5, and SO2, while underestimating NO2 and O3 values. However, it does capture a significant 24-hourly cycle as seen in the time series’ spectra in the frequency domain. Furthermore, through a student’s t-test, the model also captures a significant difference in daytime and nighttime concentrations.

The seasonal and chemical characteristic of fine particulate matter (PM2.5) was investigated in Burgos, Ilocos Norte, located at the northwestern edge of the Philippines. Each 24H-sample of fine aerosol was collected for two weeks every season. Fine particulate in the region shows strong seasonal variation in both concentration and composition. Highest mass concentration was seen during the boreal spring season with a mean mass concentration of 21.59 μg m-3, and lowest was in fall with a mean concentration of 8.44 μg m-3. Three-day wind back trajectory analysis of air mass reveals the influence of the North Western Pacific monsoon regimes on PM2.5 concentration. During southwest monsoon, sea salt is the dominant component of fine aerosols carried by moist air from the South China Sea. During northeast monsoon, on the other hand, both wind and receptor model (USEPA PMF) analysis showed that higher particulate concentration was due to the Long Range Transport (LRT) of anthropogenic emissions from the northern East Asia. Overall, sea salt and soil comprise 33 % of total PM2.5 concentration while local biomass burning makes up 33 %. LRT of industrial emission, solid waste burning and secondary sulfate from East Asia have a mean contribution of 34 % to the total fine particulate for the whole sampling period.

This study inter-compared the concentration and chemical characteristics of PM2.5 at two harbors in East Asia, and identified the potential sources of PM2.5 and their contribution. Two sites located at the Kaohsiung (Taiwan) and Manila (the Philippines) Harbors were selected for simultaneous sampling of PM2.5 in four seasons. The sampling of 24-h PM2.5 was conducted for continuous seven days in each season. Water-soluble ions, metallic elements, carbonaceous content, anhydrosugars, and organic acids in PM2.5 were analyzed to characterize their chemical fingerprints. Receptor modeling and trajectory simulation were further applied to resolve the source apportionment of PM2.5. The results indicated that the Kaohsiung Harbor was highly influenced by long-range transport (LRT) of polluted air masses from Northeast Asia, while the Manila Harbor was mainly influenced by local emissions. Secondary inorganic aerosols were the most abundant ions in PM2.5. Crustal elements dominated the metallic content of PM2.5, but trace elements were mainly originated from anthropogenic sources. Higher concentrations of organic carbon (OC) than elemental carbon (EC) was found in PM2.5, with secondary OC (SOC) dominant to the former. Levoglucosan in PM2.5 at the Manila Harbor were superior to those at the Kaohsiung Harbor due to biomass burning surrounding the Manila Harbor. Additionally, high mass ratios of malonic and succinic acids (M/S) in PM2.5 indicated the formation of SOAs. Overall, the ambient air quality of Manila Harbor was more polluted than Kaohsiung Harbor. The Kaohsiung Harbor was more severely affected by LRT of polluted air masses from Northeast Asia, while those toward the Manila Harbor came from the oceans. The major sources resolved by CMB and PMF models at the Kaohsiung Harbor were secondary aerosols, ironworks, incinerators, oceanic spray, and ship emissions, while those at the Manila Harbor were secondary aerosols, soil dust, biomass burning, ship emissions, and oceanic spray.

Tropical cyclone (TC)-induced rainfall (TC rain) in the Philippines was investigated using a combination of ground and satellite observations to produce a blended 64-yr precipitation dataset. A total of 1673 TCs were examined using best track data from the Japan Meteorological Agency. Rainfall from 100 (~1110 km) of the TC center was considered as TC-induced rainfall. TC rain contribution is highest in the northern Philippines, particularly along the western coast of Luzon (up to 54%), and lowest in the southern islands of Mindanao (6%). The high TC rain contribution is attributed to the enhancement of the Asian southwest monsoon by TCs located to the northeast of the Philippines. An unsupervised clustering method, k-means clustering, was used to divide the archipelago into four climate subtypes according to monthly rainfall variation. Interannual variability of rainfall from climate clusters with high TC rain contribution generally follows the variability of TC rain. On the other hand, the variability of low TC rain clusters is mainly influenced by El Niño–Southern Oscillation (ENSO). All clusters show increasing trends of 16.9%–19.3% decade−1 in TC rain percentage contribution since 2000. This study hypothesizes that this increasing trend is due to changes in the characteristics of TC steering mechanisms and thermodynamic properties east of the Philippines in the past one and a half decades.

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.

Two elevated fine particulate mass concentration events were observed in a span of a week in northern Philippines on March of 2017. Results from chemical characterization, lidar observation, and model simulations of particulate matter show the high aerosol concentration events to be caused by long range transport (LRT) of anthropogenic pollutants from northern East Asia at the surface and biomass burning emission from Indochina aloft. In this study, we investigated the transport path of these LRT aerosols and estimated their direct radiative forcing. A strong Siberian high and a confluent flow induced by a continental high over the main Asian continent and a cyclonic circulation over the south of Japan produced a strong northerly wind that carried pollutants from northern East Asia to northern Philippines. At the same time, strong westerlies 2–4 km aloft carried biomass burning emissions from Indochina, which also had an impact on ground concentration. Mass extinction efficiency of LRT aerosols was estimated to be in the range of 2.06–6.44 m2g-1 with a mean value of 4.32 ± 1.32 m2g−1. In addition, we calculated the direct radiative forcing effect under clear sky condition and found that the transported aerosols had a mean net negative forcing (cooling) effect of −50.80 ± 12.38 Wm−2 and −11.98 ± 3.96 Wm−2 on surface and at the top of the atmosphere, respectively. Such pollutant transport during wintertime cold surges had been shown to reduce local surface pollutants in northern China. Consequently, its impacts shift to downwind regions as far as the Philippines. As winter cold surge frequency increases due to the warming arctic, more LRT events may be expected as a result of climate change.

We present results of one-year of measurements, some interesting phenomena like CO and CH4 enhancements, and comparisons with satellite data at Burgos TCCON site in the Philippines, as well as activities involving participation in aircraft observation campaigns such as EMeRGe-Asia and NASA CAMP2Ex.

The Total Carbon Column Observing Network (TCCON) is a global network dedicated to the precise and accurate measurements of greenhouse gases (GHG) in the atmosphere. The TCCON station in Burgos, Ilocos Norte, Philippines was established with the primary purpose of validating the upcoming Greenhouse gases Observing SATellite-2 (GOSAT-2) mission and in general, to respond to the need for reliable ground-based validation data for satellite GHG observations in the region. Here, we present the first 4 months of data from the new TCCON site in Burgos, initial comparisons with satellite measurements of C O 2 and model simulations of C O . A nearest sounding from Japan’s GOSAT as well as target mode observations from NASA’s Orbiting Carbon Observatory 2 (OCO-2) showed very good consistency in the retrieved column-averaged dry air mole fractions of C O 2 , yielding TCCON - satellite differences of 0.86 ± 1.06 ppm for GOSAT and 0.83 ± 1.22 ppm for OCO-2. We also show measurements of enhanced C O , probably from East Asia. GEOS-Chem model simulations were used to study the observed C O variability. However, despite the model capturing the pattern of the C O variability, there is an obvious underestimation in the C O magnitude in the model. We conclude that more measurements and modeling are necessary to adequately sample the variability over different seasons and to determine the suitability of current inventories.

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.

Differential optical absorption spectroscopy (DOAS) is a useful technique for measuring nitrogen dioxide (NO2) and aerosol, the most important species in urban environmental pollution. This paper reports on the results of our dual path DOAS measurements recently conducted in Chiba City, Japan, using xenon flashlights equipped on tall constructions as aviation obstruction lights. Because of the proximity of the southern DOAS path to an industrial area, it is found that the level of air pollution generally increases with the dominance of westerly winds, from the plausible source area to the observation light path. This situation is consistent with the result of wind lidar measurement covering a sector of ±28? with the observation range of approximately 2.8 km. In spite of the fact that the two DOAS paths, having path lengths of 5.5 and 3.5 km each, are located in separated regions of Chiba City, the observed temporal behavior was similar for both nitrogen dioxide and aerosol, though the southern path tends to exhibit slightly higher pollution levels than the northern counterpart. Additionally it is confirmed that size information of aerosol particles can be derived from the DOAS data through the analysis of the wavelength dependence of the aerosol optical thickness, which shows fairly good correlation with the mass ratio between PM2.5 and suspended particulate matter (SPM) obtained from the in-situ sampling station measurement. Thus, the DOAS approach can also be utilized for obtaining information on PM2.5 that is considered to be more harmful to human health than SPM.

The diurnal variation of the aerosol optical depth (AOD) at 500nm wavelength and the Angstrom turbidity coefficients (α and β) were derived from direct beam measurements. Results of the measurements showed that the AOD, α, and β variations at different time intervals were mainly influenced by human activities and industrial operations as the day progressed. Hourly variability of AOD (in terms of percent departure from the daily mean) showed that it increases from morning and then reaches its maximum diversion from the mean (25%) at noontime (around 11 AM local time) and then slowly decreases in the afternoon. The wavelength exponent showed an almost similar pattern of diurnal variability as AOD. Its variation ranges at about 6 % above and below the average value of α. However β has a different pattern as compared to AOD and α. It consistently departs above the mean at around 20% in the morning and close to 30% below the mean in the afternoon. Daily values of AOD range from 0.545 to 2.174 with values frequently in the range of 0.5 to 1.5. Daily α was frequently around 1.75 but its values ranged from 0.502 to 3.236. This denoted that fine particles were dominating over coarse particles.

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.

Air pollution is not only caused by local emission but also by atmospheric pollutants being transported from the surrounding region. This study examines the impact of the downdraft at the edge of a long-lived typhoon system Parma on the transport of atmospheric pollution from the main Asian continent to the Philippines using the fully coupled Weather Research and Forecast - Chemistry (WRF-CHEM) regional air quality model. On the first week of October 2009, typhoon Parma became virtually stationary at the northern tip of the Philippines as it interacted with typhoon Melor via the Fujiwara effect, producing a rare northwest wind that provided a transport path for the pollutants. The simulation of Nitrogen Oxides (NOx: including NO and NO2) and Ozone (O3) showed that gaseous NOx do not travel very far while the secondary pollutant O3 underwent long range transport.

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.

Agriculture is a major industry in the Philippines with 50% of its work force working in agriculture. Isabela is the number one corn producer in the country, contributing 22.57% of the national production, housing 20.26% of the national area producing corn. Farmers in this area use traditions and superstitions in making farming decisions. While this worked in the past, the recent progression of climate change has prompted the need for technology to provide advice to farmers so that they may take into consideration the big factors that must be considered when farming namely the atmosphere, cultivar, soil and farm management. This paper presents a Smarter Agriculture solution through precision agriculture. This solution leverages on DSSAT 4.5 with Automated Weather Station (AWS) sensors, numerical climate model, numerical weather model, corn cultivar parameters, SMS technology as well as the expert knowledge of the farmers. The solution is designed for yellow corn farmers in Isabela where farmers have limited Internet connection and have difficulty in using smart phones hence, the use of SMS. This paper will focus on how the solution is developed so that farmers may be able to identify the best date to start planting, best date to harvest, optimal water as well as the projected dry yield for the crop, taking into consideration the climate outlook during the planting season. This allows the farmer to query about the weather outlook for short-term farming decisions such as decisions on whether to apply fertilizer or irrigation during the day.

Many volcanoes worldwide are located near populated cities that experience monsoon seasons, characterised by shifting winds each year. Because of the severity of flood impact to large populations, it is worthy of investigation in the Philippines and elsewhere to better understand the phenomenon for possible hazard mitigating solutions, if any. During the monsoon season, the change in flow direction of winds brings moist warm air to cross the mountains and volcanoes in western Philippines and cause lift into the atmosphere, which normally leads to heavy rains and floods. Heavy southwest monsoon rains from 18-21 August 2013 flooded Metro Manila (population of 12 million) and its suburbs paralyzing the nation’s capital for an entire week. Called the 2013 Habagat event, it was a repeat of the 2012 Habagat or extreme southwest monsoon weather from 6-9 August, which delivered record rains in the mega city. In both the 2012 and 2013 Habagat events, cyclones, the usual suspects for the delivery of heavy rains, were passing northeast of the Philippine archipelago, respectively, and enhanced the southwest monsoon. Analysis of Doppler data, rainfall measurements, and Weather Research and Forecasting (WRF) model simulations show that two large stratovolcanoes, Natib and Mariveles, across from Manila Bay and approximately 70 km west of Metro Manila, played a substantial role in delivering extreme rains and consequent floods to Metro Manila. The study highlights how volcanoes, with their shape and height create an orographic effect and dispersive tail of rain clouds which constitutes a significant flood hazard to large communities like Metro Manila.

Seasonal variations of tropospheric aerosol properties in Chiba, Japan, are investigated by means of optical monitoring (1999–2005), ground sampling (1998–2004), and wind data. The observation site at Chiba University is located in the urban Chiba area, about 30 km southeast of Tokyo. The aerosol optical thickness (AOT) and Angstrom exponent are derived from the data taken with a sun photometer. The value of AOT at the wavelength of 500 nm exhibits a seasonal variation, showing small values (

Continuous data of the atmosphere monitored using a portable automated lidar are correlated with the concentration of ground-measured suspended particulate matter (SPM). When the boundarylay er is well mixed, a high correlation is found between the lidar and the ground SPM data, making it possible to calculate the mass extinction efficiencyof the aerosols in the atmosphere. It is found that within the ground layer, the value of mean mass extinction efficiencychanges in a range of 4–12 m 2 g −1 , with smaller and larger values occurring for size distributions dominated bycoarse and fine particles, respectively .

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.