Abstract. Thermal structure associated with deep convective clouds is investigated using Global Positioning System (GPS) radio occultation measurements. GPS data are insensitive to the presence of clouds, and provide high vertical resolution and high accuracy measurements to identify associated temperature behavior. Deep convective systems are identified using International Satellite Cloud Climatology Project (ISCCP) satellite data, and cloud tops are accurately measured using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO) lidar observations; we focus on 53 cases of near-coincident GPS occultations with CALIPSO profiles over deep convection. Results show a sharp spike in GPS bending angle highly correlated to the top of the clouds, corresponding to anomalously cold temperatures within the clouds. Above the clouds the temperatures return to background conditions, and there is a strong inversion at cloud top. For cloud tops below 14 km, the temperature lapse rate within the cloud often approaches a moist adiabat, consistent with rapid undiluted ascent within the convective systems.

In February 2017 the "Carbonaceous Aerosol in Rome and Environs (CARE)" experiment was carried out in downtown Rome to address the following specific questions: what is the color, size, composition, and toxicity of the carbonaceous aerosol in the Mediterranean urban background area of Rome? The motivation of this experiment is the lack of understanding of what aerosol types are responsible for the severe risks to human health posed by particulate matter (PM) pollution, and how carbonaceous aerosols influence radiative balance. Physicochemical properties of the carbonaceous aerosol were characterised, and relevant toxicological variables assessed. The aerosol characterisation includes: (i) measurements with high time resolution (min to 1-2 h) at a fixed location of black carbon (eBC), elemental carbon (EC), organic carbon (OC), particle number size distribution (0.008-10 µm), major non refractory PM 1 components, elemental composition, wavelength-dependent optical properties, and atmospheric turbulence; (ii) 24-h measurements of PM 10 and PM 2.5 mass concentration, water soluble OC and brown carbon (BrC), and levoglucosan; (iii) mobile measurements of eBC and size distribution around the study area, with computational fluid dynamics modeling; (iv) characterisation of road dust emissions and their EC and OC content. The toxicological assessment includes: (i) preliminary evaluation of the potential impact of ultrafine particles on lung epithelia cells (cultured at the air liquid interface and directly exposed to particles); (ii) assessment of the oxidative stress induced by carbonaceous aerosols; (iii) assessment of particle size dependent number doses deposited in different regions of the human body; (iv) PAHs biomonitoring (from the participants into the mobile measurements). The first experimental results of the CARE experiment are presented in this paper. The objective here is to provide baseline levels of carbonaceous aerosols for Rome, and to address future research directions. First, we found that BC and EC mass concentration in Rome are larger than those measured in similar urban areas across Europe (the urban background mass concentration of eBC in Rome in winter being on average 2.6 ± 2.5 µg · m −3 , mean eBC at the peak level hour being 5.2 (95% CI = 5.0-5.5) µg · m −3 ). Then, we discussed significant variations of carbonaceous aerosol properties occurring with time scales of minutes, and questioned on the data averaging period used in current air quality standard for PM 10 (24-h). Third, we showed that the oxidative potential induced by aerosol depends on particle size and composition, the effects of toxicity being higher with lower mass concentrations and smaller particle size. Albeit this is a preliminary analysis, findings reinforce the need for an urgent update of existing air quality standards for PM 10 and PM 2.5 with regard to particle composition and size distribution, and data averaging period. Our results reinforce existing concerns about the toxicity of carbonaceous aerosols, suppo...

Abstract:In this work, the trend of the Urban Heat Island (UHI) of Rome is analyzed by both ground-based weather stations and a satellite-based infrared sensor. First, we have developed a suitable algorithm employing satellite brightness temperatures for the estimation of the air temperature belonging to the layer of air closest to the surface. UHI spatial characteristics have been assessed using air temperatures measured by both weather stations and brightness temperature maps from the Advanced Along Track Scanning Radiometer (AATSR) on board ENVISAT polar-orbiting satellite. In total, 634 daytime and nighttime scenes taken between 2003 and 2006 have been processed. Analysis of the Canopy Layer Heat Island (CLHI) during summer months reveals a mean growth in magnitude of 3-4 K during nighttime and a negative or almost zero CLHI intensity during daytime, confirmed by the weather stations.

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[1] The accurate determination of tropical cyclone (TC) cloud-top height and its vertical thermal structure using the GPS radio occultation (RO) technique is demonstrated in this study. Cloud-top heights are determined by using the bending angle anomaly and the temperature anomaly profiles during the TC events, and the results are compared to near-coincident cloud-top heights determined by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements. Based on 34 closely located RO-CALIOP pairs during 2006 to 2009, TC cloud-top heights from RO are highly correlated with CALIOP (r = 0.84), with a mean RO-CALIOP cloud-top height difference of approximately 500 m and a root-mean-square difference near 1 km. GPS RO data also allow analysis of the TC thermal structure, showing warm anomalies in the middle troposphere and cold anomalies in the upper levels, with a strong inversion near cloud top. We further investigate the thermal structure of the TCs from collocated radiosondes, and identify 246 RO-radiosonde pairs from 2001 to 2009. Radiosonde data confirm the thermal structure identified in GPS RO, with a strong inversion near the inferred cloud top. The mean difference between RO-derived inversion heights and those from radiosonde temperature profiles is approximately 500 m. Results show that, while cloud-top height detected from nadir-viewing satellites can be easily biased by a few kilometers, the biases of RO-derived cloud-top height are within~500 m.Citation: Biondi, R., S.-P. Ho, W. Randel, S. Syndergaard, and T. Neubert (2013), Tropical cyclone cloud-top height and vertical temperature structure detection using GPS radio occultation measurements,

Volcanic activity occurring in tropical moist atmospheres can promote deep convection and trigger volcanic thunderstorms. these phenomena, however, are rarely observed to last continuously for more than a day and so insights into the dynamics, microphysics and electrification processes are limited. Here we present a multidisciplinary study on an extreme case, where volcanically-triggered deep convection lasted for six days. We show that this unprecedented event was caused and sustained by phreatomagmatic activity at Anak Krakatau volcano, Indonesia during 22-28 December 2018. Our modelling suggests an ice mass flow rate of ~5 × 10 6 kg/s for the initial explosive eruption associated with a flank collapse. Following the flank collapse, a deep convective cloud column formed over the volcano and acted as a 'volcanic freezer' containing ~3 × 10 9 kg of ice on average with maxima reaching ~10 10 kg. Our satellite analyses reveal that the convective anvil cloud, reaching 16-18 km above sea level, was ice-rich and ash-poor. cloud-top temperatures hovered around −80 °C and ice particles produced in the anvil were notably small (effective radii ~20 µm). our analyses indicate that vigorous updrafts (>50 m/s) and prodigious ice production explain the impressive number of lightning flashes (~100,000) recorded near the volcano from 22 to 28 December 2018. Our results, together with the unique dataset we have compiled, show that lightning flash rates were strongly correlated (R = 0.77) with satellite-derived plume heights for this event. Tropical thunderstorms can be triggered in a variety of ways. Common triggering mechanisms include solar heating, convergence of surface winds and the flow of wind over topography 1. A less studied mechanism is in the case of an erupting volcano where the input of heat at the surface initiates deep convection 2-4. Intense heating at ground surface and entrainment of moist air generates positive buoyancy 5 , which rapidly transports volcanic gases and ash particles up to the tropopause and beyond. Here we present the first detailed account of tropical deep convection triggered and sustained by magma-seawater interactions at an island volcano. Anak Krakatau ('Child of Krakatau') is an island volcano located in Indonesia's Sunda Strait (6 °06′07″S, 105 °25′23″E) between the islands of Java and Sumatra (Fig. 1). The volcano first appeared in January 1927 having formed in the caldera left behind by the famous cataclysmic eruption of Krakatau in 1883 6. On 22 December 2018, Anak Krakatau underwent a major explosive eruption after experiencing six months of intense Strombolian to Vulcanian activity. The eruption resulted in a flank collapse on the southwestern side of the volcano 7,8 , which generated a deadly tsunami that hit the coasts of Java and Sumatra at 21:27 LT (14:27 UTC) 9. The flank collapse marked the beginning of sustained phreatomagmatic activity at the volcano and led to the formation of a deep convective plume recorded by satellite for about six days. initial explosive event. We analy...

We investigate the short-term effects of air temperature, rainfall, and socioeconomic indicators on malaria incidence across Rwanda and Uganda from 2002 to 2011. Delayed and nonlinear effects of temperature and rainfall data are estimated using generalised additive mixed models with a distributed lag nonlinear specification. A time series cross-validation algorithm is implemented to select the best subset of socioeconomic predictors and to define the degree of smoothing of the weather variables. Our findings show that trends in malaria incidence agree well with variations in both temperature and rainfall in both countries, although factors other than climate seem to play an important role too. The estimated short-term effects of air temperature and precipitation are nonlinear, in agreement with previous research and the ecology of the disease. These effects are robust to the effects of temporal correlation. The effects of socioeconomic data are difficult to ascertain and require further evaluation with longer time series. Climate-informed models had lower error estimates compared to models with no climatic information in 77 and 60% of the districts in Rwanda and Uganda, respectively. Our results highlight the importance of using climatic information in the analysis of malaria surveillance data, and show potential for the development of climateinformed malaria early warning systems. IntroductionDespite the global contraction in range over the past century , malaria still imposes a significant health and socioeconomic burden to many countries (WHO, 2013). The World Health Organization estimates that about 3.4 billion people are at risk of malaria (WHO, 2013). Approximately 207 million cases and 627,000 deaths occurred in 2012 worldwide (WHO, 2013). About 90% of the total mortality occurs in sub-Saharan Africa, and 77% of that percentage happens in children under 5 years of age (WHO, 2013). Two countries significantly affected by malaria are Rwanda and Uganda. Malaria has long been considered the main cause of morbidity and mortality in both countries (NISR, MOH and ICF International, 2012; UBOS and ICF International, 2012). Over the period 2002 to 2011, more than five million malaria cases were reported in Rwanda to government health facilities. The number was significantly greater in Uganda with about 100 million reports of suspected malaria cases between 2002 and 2010.Trends in malaria incidence could be attributed to the complex interplay of a range of determinants including climatic, environmental, and socioeconomic factors (Kazembe et al., 2006;Lowe et al., 2013;Rulisa et al., 2013). Statistical models are useful tools that allow us: i) to understand how disease outcomes change as a function of variations in their key driver; and ii) to predict disease outcomes based on the dynamics of such drivers (James et al., 2013). This paper aims to investigate the ways in which malaria incidence varies as a function of short-term changes in air temperature and rainfall over the period [2002][2003][2004][2005][...

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