Ground-based Remote Sensors Research Articles

Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy)—Direct Versus Inverse Bayesian Retrieval

CO2 is the second most abundant volatile species of degassing magma. CO2 fluxes carry information of incredible value, such as periods of volcanic unrest. Ground-based laser remote sensing is a powerful technique to measure CO2 fluxes in a spatially integrated manner, quickly and from a safe distance, but it needs accurate knowledge of the plume speed. The latter is often difficult to estimate, particularly for complex topographies. So, a supplementary or even alternative way of retrieving fluxes would be beneficial. Here, we assess Bayesian inversion as a potential technique for the case of the volcanic crater of Solfatara (Italy), a complex terrain hosting two major CO2 degassing fumarolic vents close to a steep slope. Direct integration of remotely sensed CO2 concentrations of these vents using plume speed derived from optical flow analysis yielded a flux of 717 ± 121 t day−1, in agreement with independent measurements. The flux from Bayesian inversion based on a simple Gaussian plume model was in excellent agreement under certain conditions. In conclusion, Bayesian inversion is a promising retrieval tool for CO2 fluxes, especially in situations where plume speed estimation methods fail, e.g., optical flow for transparent plumes. The results have implications beyond volcanology, including ground-based remote sensing of greenhouse gases and verification of satellite soundings.

Synergy of lidar and passive remote sensor data for retrieving profiles of microphysical properties of non-spherical particles

In this study we explore how the combination of 3 backscatter and 2 extinction lidar data with data that can be collected with ground-based and space-borne passive remote sensors, e.g. phase function coefficients which can be derived at various measurement wavelengths and scattering angles can result in improved profiles of particle microphysical properties. The algorithm is based on a light-scattering model that uses a mixture of spheres and randomly oriented spheroids.

Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan

Atmospheric aerosols are considered to be an important constituent of Earth’s atmosphere because of their climatic, environmental, and health effects. Therefore, while studying the global climate change, investigation of aerosol concentrations and properties is essential both at local and regional levels. In this paper, we have used relatively long-term Aerosol Robotic Network (AERONET) data during September 2006–August 2014 to analyze aerosol properties such as aerosol optical depth at 500 nm (AOD), Ångström exponent (440–870 nm) (AE), refractive index (RI), and asymmetry parameter over Karachi, a coastal megacity of Pakistan. The average annual values of AOD and AE were found to be 0.48 ± 0.20 and 0.59 ± 0.29, respectively. The peak (0.88 ± 0.31) AOD was recorded in July with corresponding AE of 0.30 ± 0.22 representing reasonably higher concentration of coarse size particles over Karachi. The cluster analysis using the scatter plot between absorption AE and extinction AE revealed that desert dust prevailed in the atmosphere of Karachi in spring and summer, while biomass burning aerosols dominate in autumn and winter. The peak values of volume concentrations of coarse and fine-mode particulate matter were found in summer and autumn, respectively. Also, we found significant growing trend in single-scattering albedo with wavelength, indicating the domination of dust particles during summer and spring. The peak value of the real part of the RI was observed in spring (1.53) and modest in winter (1.50). On the contrary, the peak value of the imaginary part of the RI was observed to be constantly elevated in winter and lesser in spring.

Effect of Heat Wave Conditions on Aerosol Optical Properties Derived from Satellite and Ground-Based Remote Sensing over Poland

During an exceptionally warm September in 2016, unique and stable weather conditions contributed to a heat wave over Poland, allowing for observations of aerosol optical properties, using a variety of ground-based and satellite remote sensors. The data set collected during 11–16 September 2016 was analysed in terms of aerosol transport (HYbrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT)), aerosol load model simulations (Copernicus Atmosphere Monitoring Service (CAMS), Navy Aerosol Analysis and Prediction System (NAAPS), Global Environmental Multiscale-Air Quality (GEM-AQ), columnar aerosol load measured at ground level (Aerosol Robotic NETwork (AERONET), Polish Aerosol Research Network (PolandAOD)) and from satellites (Spinning Enhanced Visible and Infrared Imager (SEVIRI), Moderate Resolution Imaging Spectroradiometer (MODIS)), as well as with 24/7 PollyXT Raman Lidar observations at the European Aerosol Research Lidar Network (EARLINET) site in Warsaw. Analyses revealed a single day of a relatively clean background aerosol related to an Arctic air-mass inflow, surrounded by a few days with a well increased aerosol load of differing origin: pollution transported from Germany and biomass burning from Ukraine. Such conditions proved excellent to test developed-in-house algorithms designed for near real-time aerosol optical depth (AOD) derivation from the SEVIRI sensor. The SEVIRI AOD maps derived over the territory of Poland, with an exceptionally high resolution (every 15 min; 5.5 × 5.5 km2), revealed on an hourly scale, very low aerosol variability due to heat wave conditions. Comparisons of SEVIRI with NAAPS and CAMS AOD maps show strong qualitative similarities; however, NAAPS underestimates AOD and CAMS tends to underestimate it on relatively clean days (<0.2), and overestimate it for a high aerosol load (>0.4). A slight underestimation of the SEVIRI AOD is reported for pixel-to-column comparisons with AODs of several radiometers (AERONET, PolandAOD) and Lidar (EARLINET) with high correlation coefficients (r2 of 0.8–0.91) and low root-mean-square error (RMSE of 0.03–0.05). A heat wave driven increase of the boundary layer height of 10% is accompanied with the AOD increase of 8–12% for an urban site dominated by anthropogenic pollution. Contrary trend, with an AOD decrease of around 4% for a rural site dominated by a long-range transported biomass burning aerosol is reported. There is a positive feedback of heat wave conditions on local and transported pollution and an extenuating effect on transported biomass burning aerosol. The daytime mean SEVIRI PM2.5 converted from the SEVIRI AODs at a pixel representative for Warsaw is in agreement with the daily mean PM2.5 surface measurements, whereby SEVIRI PM2.5 and Lidar-derived Ångström exponent are anti-correlated.

Radiative Forcing Estimation of Aerosols at an Urban Site near the Thar Desert Using Ground-Based Remote Sensing Measurements

ABSTRACT The focus of present study is to quantify the radiation budget of aerosols over Jaipur (Northwestern, India) from 2011 to 2015. The Aerosol radiative forcing (ARF) has been determined for shortwave spectrum (0.3–3.0 µm) individually for the top of the atmosphere (TOA), bottom of the atmosphere (BOA) and within the atmosphere (ATM) over study region. Santa Barbara DISORT Atmospheric Radiative Transfer model (SBDART) is used to simulate the aerosols radiative effect. The inter-annual monthly average of ARF at TOA during 2011–2015 is found between –11.40 to –5.60 W m–2, while the ARF at BOA is found to be between –32.2 to –22.49 W m–2. Likewise, the ARF within the atmosphere (ATM) comes between 14.04 to 22.47 W m–2 over Jaipur. The SBDART model is run discretely for Dust period (DSP) and non-Dust Period (NDP) during the year 2012 to inspect the change in ARF during extreme events over the Jaipur site. During DSP, the net TOA and BOA forcing are found in the range –20.71 to –16.81 W m–2 and –45.15 to –39.6 W m–2, respectively, and net ATM forcing varies in the range 22.7 to 24.4 W m–2. For the NDP, the corresponding value varies in the range –10.1 to –6.6 W m–2 and –23.6 to –22.3 W m–2. The net ATM forcing during NDP is between 12.2 to 17.05 W m–2. The value of BOA increases more than ~67% during DSP than NDP. The more increase (–ve) in surface forcing represents the cooling of the surface during DSP. The results depict that dust over Jaipur in the vicinity of the Thar Desert is scattering in nature with high value (> 0.95) of SSA. The scattering is mostly high during summer and low in winter.

Cloud Properties under Different Synoptic Circulations: Comparison of Radiosonde and Ground-Based Active Remote Sensing Measurements

In this study, long-term (10 years) radiosonde-based cloud data are compared with the ground-based active remote sensing product under six prevailing large-scale synoptic patterns, i.e., cyclonic center (CC), weak pressure pattern (WP), the southeast bottom of cyclonic center (CB), cold front (CF), anticyclone edge (AE) and anticyclone center (AC) over the Southern Great Plains (SGP) site. The synoptic patterns are generated by applying the self-organizing map weather classification method to the daily National Centers for Environmental Protection mean sea level pressure records from the North American Regional Reanalysis. It reveals that the large-scale synoptic circulations can strongly influence the regional cloud formation, and thereby have impact on the consistency of cloud retrievals from the radiosonde and ground-based cloud product. The total cloud cover at the SGP site is characterized by the least in AC and the most in CF. The minimum and maximum differences between the two cloud methods are 10.3% for CC and 13.3% for WP. Compared to the synoptic patterns characterized by scattered cloudy and clear skies (AE and AC), the agreement of collocated cloud boundaries between the two cloud approaches tends to be better under the synoptic patterns dominated by overcast and cloudy skies (CC, WP and CB). The rainy and windy weather conditions in CF synoptic pattern influence the consistency of the two cloud retrieval methods associated with the limited capabilities inherent to the instruments. The cloud thickness distribution from the two cloud datasets compares favorably with each other in all synoptic patterns, with relative discrepancy of ≤0.3 km.

Evaluation of the Amount of Nitrogen Top Dressing Based on Ground-based Remote Sensing for Leaf Perilla (Perilla frutescens) under the Polytunnel House

This study was conducted to evaluate the amount of nitrogen (N) top dressing based on the normalized difference vegetation indices (NDVI) by ground based sensors for leaf perilla under the polyethylene house. Experimental design was the randomized complete block design for five N fertilization levels and conventional fertilization with 3 and 4 replications in Gumsan-gun and Milyang-si field, respectively. Dry weight (DW), concentration of N, and amount of N uptake by leaf perilla as well as NDVIs from sensors were measured monthly. Difference of growth characteristics among treatments in Gumsan field was wider than Milyang. SPAD-502 chlorophyll meter reading explained 43.4% of the variability in N content of leaves in Gumsan field at 150 th day after seedling (DAS) and 45.9% in Milyang at 239 th DAS. Indexes of red sensor (RNDVI) and amber sensor (ANDVI) at 172 th day after seedling (DAS) in Gumsan explained 50% and 57% of the variability in N content of leaves. RNDVI and ANDVI at 31 th DAS in Milyang explained 60% and 65% of the variability in DW of leaves. Based on the relationship between ANDVI and N application rate, ANDVI at 172 th DAS in Gumsan explained 57% of the variability in N application rate but non significant relationship in Milyang field. Average sufficiency index (SI) calculated from ratio of each measurement index per maximum index of ANDVI at 172 th DAS in Gumsan explained 73% of the variability in N application rate. Although the relationship between NDVIs and growth characteristics was various upon growing season, SI by NDVIs of ground based remote sensors at top dressing season was thought to be useful index for recommendation of N top dressing rate of leaf perilla.

Retrieval of water vapor mixing ratio from a multiple channel Raman-scatter lidar using an optimal estimation method.

Lidar measurements of the atmospheric water vapor mixing ratio provide an excellent complement to radiosoundings and passive, ground-based remote sensors. Lidars are now routinely used that can make high spatial-temporal resolution measurements of water vapor from the surface to the stratosphere. Many of these systems can operate during the day and night, with operation only limited by clouds thick enough to significantly attenuate the laser beam. To enhance the value of these measurements for weather and climate studies, this paper presents an optimal estimation method (OEM) to retrieve the water vapor mixing ratio, aerosol optical depth profile, Ångstrom exponent, lidar constants, detector dead times, and measurement backgrounds from multichannel vibrational Raman-scatter lidars. The OEM retrieval provides the systematic uncertainties due to the overlap function, calibration factor, air density and Rayleigh-scatter cross sections, in addition to the random uncertainties of the retrieval due to measurement noise. The OEM also gives the vertical resolution of the retrieval as a function of height, as well as the height to which the contribution of the a priori is small. The OEM is applied to measurements made by the Meteoswiss Raman Lidar for Meteorological Observations (RALMO) in the day and night for clear and cloudy conditions. The retrieved water vapor mixing ratio is in excellent agreement with both the traditional lidar retrieval method and coincident radiosoundings.

Study of Droplet Activation in Thin Clouds Using Ground-Based Raman Lidar and Ancillary Remote Sensors

A methodology for the study of cloud droplet activation based on the measurements performed with ground-based multi-wavelength Raman lidars and ancillary remote sensors collected at CNR-IMAA observatory, Potenza, South Italy, is presented. The study is focused on the observation of thin warm clouds. Thin clouds are often also optically thin: this allows the cloud top detection and the full profiling of cloud layers using ground-based Raman lidar. Moreover, broken clouds are inspected to take advantage of their discontinuous structure in order to study the variability of optical properties and water vapor content in the transition from cloudy regions to cloudless regions close to the cloud boundaries. A statistical study of this variability leads to identify threshold values for the optical properties, enabling the discrimination between clouds and cloudless regions. These values can be used to evaluate and improve parameterizations of droplet activation within numerical models. A statistical study of the co-located Doppler radar moments allows to retrieve droplet size and vertical velocities close to the cloud base. First evidences of a correlation between droplet vertical velocities measured at the cloud base and the aerosol effective radius observed in the cloud-free regions of the broken clouds are found.

Cloud and Aerosol Interaction Observed in SKYNET Hefei Site in China

The interaction relationship between cloud and aerosol is studied via their optical depth and cloud effective radius based on ground-based remote sensors. By attenuated backscatter obtained by lidar the optical depth of cloud and aerosol is retrieved. Combing with liquid water path observed by microwave radiometer, the cloud number concentration and cloud effective radius is also retrieved based on the adiabatic hypothesis Cases studies shows that during the stable stratocumulus with interval precipitation period and aerosol with vertical motion, the cloud effective radius shows both negative and positive relationship with aerosol optical depth. It may be due to the difference of liquid water path of the cloud properties and shows complex interaction with aerosol.

Applying Advanced Ground-Based Remote Sensing in the Southeast Asian Maritime Continent to Characterize Regional Proficiencies in Smoke Transport Modeling

ABSTRACTThis work describes some of the most extensive ground-based observations of the aerosol profile collected in Southeast Asia to date, highlighting the challenges in simulating these observations with a mesoscale perspective. An 84-h WRF Model coupled with chemistry (WRF-Chem) mesoscale simulation of smoke particle transport at Kuching, Malaysia, in the southern Maritime Continent of Southeast Asia is evaluated relative to a unique collection of continuous ground-based lidar, sun photometer, and 4-h radiosonde profiling. The period was marked by relatively dry conditions, allowing smoke layers transported to the site unperturbed by wet deposition to be common regionally. The model depiction is reasonable overall. Core thermodynamics, including land/sea-breeze structure, are well resolved. Total model smoke extinction and, by proxy, mass concentration are low relative to observation. Smoke emissions source products are likely low because of undersampling of fires in infrared sun-synchronous satellite products, which is exacerbated regionally by endemic low-level cloud cover. Differences are identified between the model mass profile and the lidar profile, particularly during periods of afternoon convective mixing. A static smoke mass injection height parameterized for this study potentially influences this result. The model does not resolve the convective mixing of aerosol particles into the lower free troposphere or the enhancement of near-surface extinction from nighttime cooling and hygroscopic effects.

Exploiting Existing Ground-Based Remote Sensing Networks to Improve High-Resolution Weather Forecasts

Abstract A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.

Evaluating Light Rain from Satellite- and Ground-Based Remote Sensing Data over the Subtropical North Atlantic

AbstractThree state-of-the-art satellite climatologies are analyzed for their ability to observe light rain from predominantly shallow, warm clouds over the subtropical North Atlantic Ocean trade winds (1998–2005). HOAPS composite (HOAPS-C), version 3.2; TMPA, version 7; and GPCP 1 Degree Daily (1DD), version 1.2, are compared with ground-based S-Pol radar data from the Rain in Cumulus over the Ocean (RICO; winter 2004/05) campaign and Micro Rain Radar data from the Barbados Cloud Observatory (2010–12). Winter rainfall amounts to one-third of annual rainfall, whereby light rain from warm clouds dominates. Daily rain occurrence and rain intensity during RICO largely differ among the satellite climatologies. TMPA best captures the frequent light rain events, only missing 7% of days on which the S-Pol radar detects rain, whereas HOAPS-C misses 33% and GPCP 1DD misses 56%. Algorithm constraints mainly cause these differences. In HOAPS-C also few available passive microwave (PMW) sensor overpasses limit its performance. TMPA outperforms HOAPS-C when only comparing nonmissing time steps, yet HOAPS-C can detect rain for S-Pol rain-covered areas down to 2%. In GPCP 1DD’s algorithm, the underestimated rain occurrence derived from PMW scanners is linked to the overestimated rain intensity, being constrained by the GPCP monthly satellite–gauge combination, whereby IR sensors determine the timing. Algorithm improvements in version 1.2 increased the rain occurrence by 50% relative to version 1.1. In version 7 of TMPA, algorithm corrections in PMW sounder data largely improved the rain detection relative to version 6. TMPA best represents light rain in the North Atlantic trades, followed by HOAPS-C and GPCP 1DD.

Determining Canopy Growth Conditions of Paddy Rice via Ground-based Remote Sensing

Abstract : This study aimed to investigate the canopy growth conditions and the accuracy of phenologicalstages of paddy rice using ground-based remote sensing data. Plant growth variables including Leaf Area Index(LAI) and canopy reflectance of paddy rice were measured at the experimental fields of Chonnam NationalUniversity, Gwangju, Republic of Korea during the crop seasons of 2011, 2012, and 2013. LAI values werealso determined based on correlations with Vegetation Indices (VIs) obtained from the canopy reflectance.Three phenological stages (tillering, booting, and grain filling) of paddy rice could be identified using VIs anda spatial index (NIR versus red). We found that exponential relationships could be applied between LAI andthe VIs of interest. This information, as well as the relationships between LAI and VIs obtained in the presentstudy, could be used to estimate and monitor the relative growth and development of rice canopies during thegrowing season. Key Words : canopy growth, paddy rice, phenology, remote sensing, spatial index, vegetation index

Estimation of Nitrogen Uptake and Yield of Tobacco (Nicotiana tobacum L.) by Reflectance Indices of Ground-based Remote Sensors

Ground-based remote sensing can be used as one of the non-destructive, fast, and real-time diagnostic tools for predicting yield, biomass, and nitrogen stress during growing season. The objectives of this study were: 1) to assess biomass and nitrogen (N) status of tobacco (Nicotiana tabacum L.) plants under N stress using ground-based remote sensors; and 2) to evaluate the feasibility of spectral reflectance indices for estimating an application rate of N and predicting yield of tobacco. Dry weight (DW), N content, and N uptake at the 40th and 50th day after transplanting (DAT) were positively correlated with chlorophyll content and normalized difference vegetation indexes (NDVIs) from all sensors (P th DAT ranged from 0.72 to 1.0 and showed the same positively quadratic regression with N application rate explaining 84% for the variability of N rate. These results suggest that use of reflectance indices measured with ground-based remote sensors may assist in determining application rate of fertilizer N at the critical season and estimating yield in mid-season.

Ground-Based Remote Sensing of the ABL Structure in Moscow and Its Use to Estimate Pollutant Surface Emission Rates

AbstractCurrently used methods to estimate surface pollutant emissions require a set of specific air-sampling surveys. Data from a network of ground-based sodars and a network of air-quality stations in Moscow, Russia, are used to estimate the emission rates of carbon monoxide (CO) and nitric oxide (NO). The sodar network, consisting of three “LATAN-3” Doppler sodars and three “MTP-5” microwave temperature profilers, is used to measure the vertical profiles of vertical and horizontal wind velocity, wind direction, and temperature, which are used to determine the average mixing-layer height. The network of ground-based air-quality stations, consisting of 17 automated stations distributed uniformly across Moscow, continuously measured the CO and NO concentrations. This study focuses on an anticyclonic episode of high surface pressure over Moscow during 30 July–1 August 2012. After sunrise, the solar-induced convection effectively moderated the pollutant levels in the lowest 100–200 m. After sunset, convective mixing stopped and the wind weakened, which allowed CO and NO to reach hazardous levels. With an assumption of an average mixing-layer height of 150 m, the resulting estimate of surface emission of CO is ~6 μg m−2 s−1, whereas that for NO is ~0.6 μg m−2 s−1.

Cloud Microphysical Properties Retrieved from Downwelling Infrared Radiance Measurements Made at Eureka, Nunavut, Canada (2006–09)

AbstractThe radiative properties of clouds are related to cloud microphysical and optical properties, including water path, optical depth, particle size, and thermodynamic phase. Ground-based observations from remote sensors provide high-quality, long-term, continuous measurements that can be used to obtain these properties. In the Arctic, a more comprehensive understanding of cloud microphysics is important because of the sensitivity of the Arctic climate to changes in radiation. Eureka, Nunavut (80°N, 86°25′W, 10 m), Canada, is a research station located on Ellesmere Island. A large suite of ground-based remote sensors at Eureka provides the opportunity to make measurements of cloud microphysics using multiple instruments and methodologies. In this paper, cloud microphysical properties are presented using a retrieval method that utilizes infrared radiances obtained from an infrared spectrometer at Eureka between March 2006 and April 2009. These retrievals provide a characterization of the microphysics of ice and liquid in clouds with visible optical depths between 0.25 and 6, which are a class of clouds whose radiative properties depend greatly on their microphysical properties. The results are compared with other studies that use different methodologies at Eureka, providing context for multimethod perspectives. The authors’ findings are supportive of previous studies, including seasonal cycles in phase and liquid particle size, weak temperature–phase dependencies, and frequent occurrences of supercooled water. Differences in microphysics are found between mixed-phase and single-phase clouds for both ice and liquid. The Eureka results are compared with those obtained using a similar retrieval technique during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment.

Characterisation of vertical BrO distribution during events of enhanced tropospheric BrO in Antarctica, from combined remote and in-situ measurements

Tropospheric BrO was measured by a ground-based remote-sensing spectrometer at Halley in Antarctica in spring 2007, and BrO was measured by satellite-borne remote-sensing spectrometers using similar spectral regions and similar Differential Optical Absorption Spectroscopy (DOAS) analyses. Near-surface BrO was simultaneously measured in situ at Halley by Chemical Ionisation Mass Spectrometer (CIMS), and in an earlier year near-surface BrO was measured at Halley over a long path by a ground-based DOAS spectrometer. During enhancement episodes, total amounts of tropospheric BrO from the ground-based remote-sensor were similar to those from space, but if we assume that the BrO was confined to the mixed layer they were very much larger than values measured by either near-surface technique. This large apparent discrepancy can be resolved if substantial amounts of BrO were in the free troposphere during most enhancement episodes. Amounts observed by the ground-based remote sensor at different elevation angles, and their formal inversions to vertical profiles, demonstrate that much of the BrO was indeed often in the free troposphere. This is consistent with the ~5 day lifetime of Bry and with the enhanced BrO observed during some Antarctic blizzards.

Ground-Based Polarimetric Remote Sensing of Dust Aerosol Properties in Chinese Deserts near Hexi Corridor

One-year observation of dust aerosol properties near Hexi Corridor was obtained from polarimetric measurements by ground-based sunphotometer in the county of Minqin in northwestern China from March 2012 to February 2013. We observed an annual mean AOD of0.22±0.22at 0.50 μm and Ångström exponents of 0.1–1.0 fitting a bimode normal distribution centered at 0.18 and 0.50, respectively. The effective radii of fine (0.13–0.17 μm) and coarse (2.49–3.49 μm) modes were found stable at all seasons together with the appearance of a third mode of particle radius at 0.4–1.0 μm when AOD was larger than 0.6. It is noticeable that the real (1.5–1.7) and imaginary (0.0005 to 0.09) parts of complex refractive indices were higher than other studies performed in other desert regions of China, while single scattering albedo was relatively lower (~0.84–0.89) at wavelengths of 0.44, 0.67, 0.87, and 1.02 μm. This is partially due to calcite or hematite in the soil in Minqin or the influence of anthropogenic aerosols containing carbon. Moreover, from our novel polarimetric measurement, the scattering phase function (F11) and degree of linear polarization for incident unpolarized light (-F12/F11) of dust aerosols were also obtained within this deserted area.

Driftsondes: Providing In Situ Long-Duration Dropsonde Observations over Remote Regions

Constellations of driftsonde systems— gondolas floating in the stratosphere and able to release dropsondes upon command— have so far been used in three major field experiments from 2006 through 2010. With them, high-quality, high-resolution, in situ atmospheric profiles were made over extended periods in regions that are otherwise very difficult to observe. The measurements have unique value for verifying and evaluating numerical weather prediction models and global data assimilation systems; they can be a valuable resource to validate data from remote sensing instruments, especially on satellites, but also airborne or ground-based remote sensors. These applications for models and remote sensors result in a powerful combination for improving data assimilation systems. Driftsondes also can support process studies in otherwise difficult locations—for example, to study factors that control the development or decay of a tropical disturbance, or to investigate the lower boundary layer over the interior Antarctic continent. The driftsonde system is now a mature and robust observing system that can be combined with flight-level data to conduct multidisciplinary research at heights well above that reached by current research aircraft. In this article we describe the development and capabilities of the driftsonde system, the exemplary science resulting from its use to date, and some future applications.