This thesis presents a development to the technique of rotational Raman lidar by, incorporating an imaging spectrometer in conjunction with a clocking CCD detection system. This allowed the rotational Raman spectra of nitrogen and oxygen to be simultaneously recorded as a function of altitude. The rotational Raman spectra were uses to calculate temperature profiles. Recording the complete band envelopes of the rotational Raman spectra removed the need for an external reference, such as a radiosonde. Results are presented from measurements made in Cambridge in chapter 4 and Ny-Alesund in chapter 6. Chapter 7 presents some conventional lidar backscatter measurements made using a PMT in Birmingham during the winter part of the pollution in the Urban Midlands Area (PUMA) campaign. These measurements were used to determine the cloud base and the planetarty boundary layer height. Two automated algorithms were tested at retrieving the PBL height, the inflection point method and the centroid method.

Canopy structure has long been thought to influence the productivity and ecological dynamics of tropical forests by altering the availability of light to leaves. Theories and methods that can connect detailed quantitative observations of canopy structure with forest dynamics, however, have been lacking. There is urgent need to resolve this uncertainty because human-caused climate change may alter canopy structure and function in the Amazon. This work addresses this problem by, first, developing methods based on LiDAR remote sensing of fine-scale structural variation to predict the spatial structure of leaf area and light in forest canopies of the central Amazon (Appendices B&C). I show that LiDAR-based leaf area and light estimates can be used to predict the productivity of tree size groups and one-hectare forest plots--as well as differences between 2 sites separated by 500km (App. B). Sites also differed in canopy structure and the distribution of tree frequencies over size (size or diameter distribution). A model based on tree architecture, however, was able to connect observed differences in canopy architecture with size distributions to predict plot and site differences (App. D). This model showed that tree architecture is plastic in different light environments. While plasticity may increase light absorption, the smallest size groups appeared light limited. Absorption over size groups in one site, but not the other, agreed with the hypothesis of energetic equivalence across size structure. Ultimately, the performance of individual trees of different sizes in different canopy environments links forest demography with canopy structure and ecosystem function--I present a study aimed at improving tests of individual level theories for the role of light dependence in tree growth (App. A). Together, this work quantitatively connects canopy structure with forest carbon dynamics and demographic structure and further develops LiDAR as premier tool for studying forest ecological dynamics. Assessing variation in biomass growth and demographic structure over tropical landscapes with remote sensing will improve understanding of ecosystem function and the role of the Amazon in global Carbon dynamics.

Magmatism is a dominant process on Earth and Mars that has significantly modified and evolved the lithospheres of each planet by delivering magma to shallow depths and to the surface. Two common modes of volcanism are present on both Earth and Mars: central-vent dominated volcanism that creates large edifices from concentrating magma in chambers before eruptions and distributed volcanism that creates many smaller edifices on the surface through the independent ascent of individual magmatic dikes. In regions of distributed volcanism, clusters of volcanoes develop over thousands to millions of years. This dissertation explores the geology of distributed volcanism on Earth and Mars from shallow depths (~1 km) to the surface. On long time scales, distributed volcanism emplaces magmatic sills below the surface and feeds volcanoes at the surface. The change in spatial distribution and formation rate of volcanoes over time is used to infer the evolution of the source region of magma generation. At short time scales, the emplacement of lava flows in these fields present an urgent hazard for nearby people and infrastructure. I present software that can be used to simulate lava flow inundation and show that individual computer codes can be validated using real-world flows. On Mars, distributed volcanism occurs in the Tharsis Volcanic Province, sometimes associated with larger, central-vent shield volcanoes. Two volcanic fields in this province are mapped here. The Syria Planum field is composed three major volcanic units, two of which are clusters of 10s to >100 shield volcanoes. This area had volcanic activity that spanned 900 million years, from 3.5-2.6 Ga. The Arsia Mons Caldera field is associated with a large shield volcano. Using crater age-dating and mapping stratigraphy between lava flows, activity in this field peaked at ~150 Ma and monotonically waned until 10-90 Ma, when volcanism likely ceased.

Aerosol-cloud interactions are the most uncertain component of the anthropogenic radiative forcing. A substantial part of this uncertainty comes from the limitations of currently used spaceborne CCN proxies that (i) are column integrated and do not guarantee vertical co-location of aerosols and clouds, (ii) have retrieval issues over land, and (iii) do not account for aerosol hygroscopicity. A possible solution to overcome these limitations is to use height-resolved measurements of the spaceborne lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. This thesis presents a novel CCN retrieval algorithm based on Optical Modelling of CALIPSO Aerosol Microphysics (OMCAM) that is designed particularly for CALIPSO lidar measurements, along with its validation with airborne and surface in-situ measurements. \noindent OMCAM uses a set of normalized size distributions from the CALIPSO aerosol model and modifies them to reproduce the CALIPSO measured aerosol extinction coefficient. It then uses the modified size distribution and aerosol type-specific CCN parameterizations to estimate the number concentration of CCN (nCCN) at different supersaturations. The algorithm accounts for aerosol hygroscopicity by using the kappa parametrization. Sensitivity studies suggest that the uncertainty associated with the output nCCN may range between a factor of 2 and 3. OMCAM-estimated aerosol number concentrations (ANCs) and nCCN are validated using temporally and spatially co-located in-situ measurements. In the first part of validation, the airborne observations collected during the Atmospheric Tomography (ATom) mission are used. It is found that the OMCAM estimates of ANCs are in good agreement with the in-situ measurements with a correlation coefficient of 0.82, an RMSE of 247.2 cm-3, and a bias of 44.4 cm-3. The agreement holds for all aerosol types, except for marine aerosols, in which the OMCAM estimates are about an order of magnitude smaller than the in-situ measurements. An update of the marine model in OMCAM improve the agreement significantly. In the second part of validation, the OMCAM-estimated ANC and nCCN are compared to measurements from seven surface in-situ stations covering a variety of aerosol environments. The OMCAM-estimated monthly nCCN are found to be in reasonable agreement with the in-situ measurements with a 39 % normalized mean bias and 71 % normalized mean error. Combining the validation studies, the algorithm outputs are found to be consistent with the co-located in-situ measurements at different altitude ranges over both land and ocean. Such an agreement has not yet been achieved for spaceborne-derived CCN concentrations and demonstrates the potential of using CALIPSO lidar measurements for inferring global 3D climatologies of CCN concentrations related to different aerosol types.:1 Introduction . . . . . . . . . . . . . . . 1 1.1 Background: Aerosols in the climate system . . . . . . . . . . . . . . . . . 1 1.1.1 Aerosol-induced effective radiative forcing . . . . . . . . . . . . . . 3 1.1.2 Significance of aerosol-cloud interactions . . . . . . . . . . . . . . . 3 1.2 Observation-based ACI studies . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 In-situ studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 Spaceborne studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Spaceborne CCN proxies and their limitations . . . . . . . . . . . . . . . . 8 1.4 CCN concentrations from lidars . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Objective: CCN from spaceborne lidar . . . . . . . . . . . . . . . . . . . . 11 2 Paper 1: Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements . . . . . . . . . . . . . . . 15 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2 Data and retrievals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 CALIPSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.2 MOPSMAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.3 POLIPHON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3.1 Aerosol size distribution . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3.2 Aerosol hygroscopicity . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.3 CCN parameterizations . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.4 Application of OMCAM to CALIPSO retrieval . . . . . . . . . . . 23 2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.4.1 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.4.2 Comparison with POLIPHON . . . . . . . . . . . . . . . . . . . . . 30 2.4.3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5 Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 Paper 2: Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements . . . . . . . . . . . . . . . 39 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2 Data, retrievals, and methods . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2.1 ATom 3.2.2 CALIOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2.3 Aerosol number concentration from CALIOP . . . . . . . . . . . . 44 3.2.4 Data matching and comparison . . . . . . . . . . . . . . . . . . . . 48 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.1 Example cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.2 General findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4 Paper 3: Assessment of CALIOP-derived CCN concentrations by in situ surface measurements . . . . . . . . . . . . . . . 65 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.2 Data and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.1 In situ observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.2 CALIOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.3 Comparison Methodology . . . . . . . . . . . . . . . . . . . . . . . 71 4.3 Comparison of CCN Concentrations . . . . . . . . . . . . . . . . . . . . . . 73 4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Summary and conclusions . . . . . . . . . . . . . . . 79 6 Outlook . . . . . . . . . . . . . . . 83 References . . . . . . . . . . . . . . . 88 List of Abbreviations . . . . . . . . . . . . . . . 107 List of Variables . . . . . . . . . . . . . . . 109 List of Figures . . . . . . . . . . . . . . . 111 List of Tables . . . . . . . . . . . . . . . 113 A List of Publications . . . . . . . . . . . . . . . 115 B Acknowledgements . . . . . . . . . . . . . . . 117