Boundary Layer Water Vapor Quantification Using the Orbiting

Boundary Layer Water Vapor Quantiﬁcation using the Orbiting Carbon Observatory-2 (OCO-2) and the Atmospheric Infrared Sounder (AIRS)

Madison Shogrin

A thesis submitted to the University of Colorado at Boulder in partial fulﬁlment of the requirements to receive Honors designations in Environmental Studies

Thesis Advisor: Julie Lundquist, Department of Atmospheric and Oceanic Sciences

Honors Thesis Committee: Sebastian Schmidt, Department of Atmospheric and Oceanic Sciences Dale Miller, Department of Environmental Studies John Cassano, Department of Atmospheric and Oceanic Sciences

Outside Committee Members: Vivienne Payne, Gregory Osterman, Robert Nelson, at the NASA Jet Propulsion Laboratory / California Institute of Technology

c 2020 by Madison Shogrin ii iii

Preface

This work was supported by the National Aeronautics and Space Administration Jet Propulsion Laboratory / California Institute of Technology as a part of a summer internship through the California Institute of Technology Student-Faculty Programs Summer Internship Program (SFP-SIP) and the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) under mentor Vivienne Payne and co-mentor Gregory Osterman and Postdoctoral researcher Robert Nelson. This study was then continued for the purpose of this honors thesis made possible by JPL remote afﬁliation. The remainder of this undergraduate honors thesis was supported under Julie Lundquist, Dale Miller, Sebastian Schmidt, and John Cassano at the University of Colorado, Boulder’s Environmental Studies and Atmospheric and Oceanic Sciences departments. PREFACE iv

Acknowledgements

I would like to express my sincere gratitude to Vivienne, Greg, Rob, and everyone at JPL who made this work possible. My experience at JPL has made a huge positive inﬂuence on my desired career path. I am sincerely grateful for the continued support at CU from my thesis committee. I would like to express my sincere gratitude to my primary advisor, Julie Lundquist, and to my committee, Dale Miller, John Cassano, and Sebastian Schmidt. I am so appreciative of your help and support through this process. This thesis experience has inspired me to peruse a graduate degree in atmospheric science beginning in the fall. I plan to continue to work with satellite data to further understanding of earth’s climate. I am so excited and could not have gotten to this point without those involved in this thesis. Thank you for the constant inspiration and support. v

Abstract

Accurate quantification of water vapor in the lower troposphere is critical for numerical weather prediction and climate studies. Water vapor is the most important greenhouse gas in the atmosphere with respect to climate feedback. Water vapor’s natural regulation and large variability with space and time makes it difficult to quantify on a global scale. Ground-based in-situ measurements of water vapor are historically accurate, however, lack the global coverage needed to fully understand water vapor’s role in the greater climate system. Space-based satellite observations of water vapor in the lower atmosphere have the potential to improve temporal and spatial resolution of water vapor measurements, allowing for a deeper understanding of its role in Earth’s mechanisms of radiative forcing. Here we combine water vapor products from the Atmospheric Infrared Sounder (AIRS) and the Orbiting Carbon Observatory-2 (OCO-2) using a differencing method to quantify water vapor in the lowermost region of the troposphere; the planetary boundary layer. These calculations are then validated against radiosonde-based mergesonde products sourced from the Atmospheric Radiation Measurement (ARM) user facilities at the Southern Great Plains (SGP) site. Upon analysis, we see that the validation yielded to have extremely high correlation. Accurate quantification of lower tropospheric water vapor from space is crucial for improving numerical weather prediction and understanding Earth’s mechanisms of heat and energy transfer. vi

Contents

Preface iii

Abstractv

1 Introduction1

2 Background2

2.1 Atmospheric Water Vapor...... 2 2.2 Planetary Boundary Layer ...... 6 2.2.1 Types of Boundary Layers ...... 9 2.3 Remote Sensing...... 11 2.3.1 Remote Sensing: Overview ...... 11 2.3.2 Electromagnetic Radiation ...... 15 2.3.3 Interactions with Radiation ...... 17 2.3.4 Atmospheric Radiative Transfer ...... 20

3 Data sets 27

3.1 The Orbiting Carbon Observatory-2 ...... 29 3.1.1 Modes of Operation ...... 30 CONTENTS vii

3.1.2 Measurements ...... 32 3.1.3 Total Column Water Vapor Product ...... 33 3.2 The Atmospheric Infrared Sounder...... 36 3.2.1 Measurements ...... 37 3.2.2 Integrated Total Column Water Vapor ...... 37 3.3 Water Vapor Number Density...... 39 3.4 ARM Mergesonding Value-Added Product...... 40

4 Methodology 42

4.1 Partial Column Water Vapor Calculation...... 42 4.2 Boundary Layer Height...... 44 4.2.1 Bulk Richardson Number Method ...... 44 4.3 Validation...... 46

5 Results and Discussion 48

5.1 Results...... 48 5.2 Discussion...... 51

6 Conclusions 54

6.1 Future Work...... 55

Bibliography 56 1

1. Introduction

In this study I combine data products from the Orbiting Carbon Observatory-2 (OCO-2) and the Atmospheric Infrared Sounder (AIRS) to quantify water vapor in the lower-most region of the troposphere; planetary boundary layer. Integrated proﬁles of upper column water vapor from the AIRS instrument are subtracted from the total column water vapor product from the OCO-2 satellite to obtain a boundary layer water vapor product. The height of the boundary layer was calculated using the Bulk Richardson Number Method. The results were then validated using radiosonde integrated boundary layer water vapor products. The purpose of this study is to validate this method of space-based boundary layer water vapor quantiﬁcation in order to answer the following research question: How accurately can we quantify water vapor in the lower-most portion of the troposphere, the planetary boundary layer, on a global scale using space-based satellite measurements in two different regions of the infrared spectrum? 2

2. Background

In this section I will discuss the importance of atmospheric water vapor, signiﬁcance of the boundary layer, and fundamental information needed to make satellite measurements of water vapor in the atmosphere. This information is necessary for understanding the motivation behind this study and the obtained results.

2.1 Atmospheric Water Vapor

Water vapor is the most dominant greenhouse gas in the atmosphere, accounting for about 60 percent of the natural greenhouse effect for clear skies (Kiehl and Trenberth, 1997). Water vapor concentration ranges from 0.1 to 4 percent of total atmospheric composition at any given time and space. Water vapor is under the natural regulation of the hydrologic cycle. This natural regulation attributes to its high variability. Water vapor in the hydrologic cycle is the principle medium of energy exchange between the earth’s spheres: the cryosphere, biosphere, ocean, and atmosphere. Atmospheric water vapor is a major component of the earth’s hydrologic cycle, CHAPTER 2. BACKGROUND 3

despite representing only a small part of earth’s H2O species (Starr, 1991). The transfer of water vapor across the earth-atmosphere interface through evaporation, sublimation, evapotranspiration, condensation, precipitation, and atmospheric transport represents one of the most rapid and effective coupled mechanisms of transport within the earth’s system (Starr, 1991). The residence time of water vapor in the atmosphere ranges anywhere from minutes, to hours, to days. An amount of water vapor equivalent to the total global tropospheric reservoir is exchanged across the earth-atmosphere interface on a 10 day cycle (Starr, 1991). 99.13 percent of atmospheric water vapor resides in the troposphere, where about 80 percent of this is located in the planetary boundary layer (Leinweber, 2010). See figure 2.1 for a visual representation of atmospheric gas profiles. The vertical structure of water vapor in the atmosphere is dominated by a decrease with pressure. The global distribution of water vapor plotted along the Orbiting Carbon Observatory-2 (OCO-2) global ground track is shown in figure 2.2 and shown by the Atmospheric Infrared Sounder (AIRS) in figure 2.3. The global distribution of water vapor is concentrated at the poles and decreases poleward. The Inter-Tropical Convergence Zone (ITCZ) is a region defined by tropical convection and low atmospheric pressure and extremely high water vapor content. This region contains the highest concentrations of global water vapor. CHAPTER 2. BACKGROUND 4

Figure 2.1: Shown are atmospheric proﬁles of numerous trace gases. It is aparent that water vapor uniquely has a steep exponential decline with height. Image credit: Leinweber, 2010

The concentration of water vapor is under natural regulation. Therefore, the abundance of atmospheric water vapor is under the control of climate feedback and the hydrologic cycle. Water vapor molecules absorb and emit longwave infrared radiation emitted by the earth. Water vapor therefore has capacity for positive feedback. As the climate warms, more evaporation occurs, warmer air has a larger capacity for molecular water vapor and greenhouse trapping of infrared radiation increases. Warmer air has a higher capacity to hold water vapor by the Clausius-Clapeyron relationship:

L 1 1 es = e0exp[ [( ) − ( )]] (2.1) Rv T0 T where es in equation (2.1) is the saturation vapor pressure, L is the latent heat, Rv is

−1 −1 the water vapor gas constant of 461 JKg K , T0 is 273.15 K, e0 is 0.6113 kPa CHAPTER 2. BACKGROUND 5 and T is the current temperature. The Clausius-Clapeyron relationship describes that unsaturated air will become more unsaturated as it is warmed. Water vapor absorbs and re-emits infrared radiation; therefore attributing to the greenhouse effect. Water vapor has a positive feedback capability of 2 Wm−2K−1 (Dessler et al., 2008). Water vapor’s capacity for radiative feedback makes it one of the strongest ampliﬁers for anthropogenic climate change. This makes quantiﬁcation of atmospheric water vapor vital for understanding earth system change.

Figure 2.2: Global total column water vapor measurements plotted over OCO-2 ascending orbital track for one day during the southern hemisphere summer. Higher measurements of water vapor are concentrated slightly south of the equator. Gaps in data are likely due to cloud cover. Plotted over NASA MODIS "Blue Marble 2002". CHAPTER 2. BACKGROUND 6

Figure 2.3: Total precipital water vapor for May 2009. Image credit: NASA/JPL

The use of satellite data in weather forecasting models will continue to increase as satellite remote sensing methods of observation increase in temporal and spatial resolution and validity. Satellite observations of atmospheric properties exist with high spatial resolution and coverage and reasonable temporal resolution (Albert et al., 2005). Water vapor’s strong capacity for radiative feedback makes its abundancy within the atmospheric column important to know with high accuracy for the purpose of furthering knowledge in weather forecasting and climate change.

2.2 Planetary Boundary Layer

The Planetary Boundary Layer (PBL) is the lower-most region of the atmosphere between the earth’s surface and the free-troposphere. The PBL serves as the direct CHAPTER 2. BACKGROUND 7 interface between earth’s surface and the free atmosphere, making this region the key regulator of heat, energy, moisture and trace gas exchange between the land surface and above atmosphere through mechanisms of air-mass turbulent transport. More than 80 percent of the total mass of atmospheric water vapor resides in the boundary layer, making knowledge of total abundance of water vapor in this region important for understanding water vapor’s role in the climate system (Trent et al., 2018). (See figure 2.1). The boundary layer is directly influenced by earth’s surface, thus it serves as the medium for transport of water vapor through the atmospheric column. The height of the boundary layer ranges from 0.3 to 3 kilometers and is dependant on the thermodynamic state of the atmosphere and the coupled surface properties. The height of the PBL is highly variable and varies with diurnal and seasonal cycles. The PBL is defined by the presence of consistent turbulence. The multi-variable dependency of boundary layer height makes it difficult to quantify; however, for the purpose of this study, the top of the boundary layer is assumed to be the point in the atmospheric column where turbulence due to surface influence halts and a parcel of air becomes neutrally buoyant. A parcel of air is considered to be neutrally buoyant when it is no longer under the control of adiabatic processes. Put simply, the parcel is the same temperature as the surrounding atmospheric column (Stull, 2017). CHAPTER 2. BACKGROUND 8

Figure 2.4: Planetary Boundary Layer is the region between the surface and the free troposphere, ranging from 0.3 to 3 km high within the atmospheric column. A number density proﬁle of water vapor is plotted here in red, showing that over 80 percent of the mass of total atmospheric water vapor is found in the PBL.

The height of the boundary layer can be defined by numerous different methods depending on inversion and the presence of the low-level jet (LLJ). This study uses the Bulk Richardson Number method in order to quantify the height of the boundary layer. The Bulk Richardson Number method assumes that the height of the PBL is defined as the region within the atmospheric column where turbulence stops and a parcel of air becomes neutrally buoyant with the surrounding atmosphere. For convective boundary layers (CBL), the boundary layer top is located at the base of the overlying inversion layer that caps rising convective thermals (Liu and Liang, 2010). For stable boundary layers (SBL) the top of the boundary layers is located at the top of the underlying inversion layer, where turbulent influence from the surface nearly ceases (Liu and Liang, 2010). Boundary layer water vapor is highly abundant and influences short-term weather system development and long-term CHAPTER 2. BACKGROUND 9 climatic influences. The water vapor in the PBL therefore influences the vapor concentration of the rest of the atmosphere, making accurate measurements of water vapor in this layer extremely important for understanding Earth’s water cycle and energy transfer processes.

2.2.1 Types of Boundary Layers

Static stability controls the formation of the planetary boundary layer and influences wind and temperature profiles (Stull, 2017). Briefly, atmospheric static stability is described by the process: if a parcel of air is warmer than the surrounding atmosphere, it has positive buoyancy and will rise. Conversely, if the parcel is cooler than the surrounding atmosphere at the same height or pressure, the parcel has negative buoyancy and will sink. A parcel of air that is the same temperature as the surrounding air has neutral buoyancy and will stay (Stull, 2017). An environment is statically stable if the buoyant forces acting on a displaced air parcel push it back to its starting altitude (Stull, 2017). The height of the boundary layer is directly dependant on the temperature of the surface, as temperature is the driver of the buoyant force acting on parcels of air. The temperature of the earth’s surface influences atmospheric convection and turbulence within the boundary layer. The height of the boundary layer is highly variable and is dominated by diurnal cycles (Liu and Liang, 2010). The boundary layer is typically shallow at night, as the surface layer becomes stable through the infrared radiative cooling of the earth. The boundary layer grows deep during the day as solar warming creates convective unstable conditions (Liu and Liang, 2010). CHAPTER 2. BACKGROUND 10

Figure 2.5: The boundary layer with its three main types of sub-layers, the convective boundary (mixed) layer, the less turbulent residual layer containing the former CBL air and the nocturnal stable boundary layer. Image credit: Stull, 2017

The boundary layer structure during its diurnal cycle can be further divided into three groups: the convective boundary layer (CBL), the stable boundary layer (SBL), and the residual layer (RL) (Stull, 2017). See ﬁgure 2.5 for diagram of boundary layers types. The convective boundary layer exists during the day time and is dominated by convective thermal uplift generated as a result of a heated surface. The CBL occurs under conditions with light winds and a warmer surface, so this is common on a sunny day in fair weather (Stull, 2017). A CBL can also form when cool air blows over a warm surface. Both of these conditions stimulate turbulent convection leading to statically unstable conditions within the boundary layer. The stable boundary layer often forms during the night due to the effects of infrared radiative cooling of the surface. The SBL can also occur when warm air is advected over a cool surface (Liu and Liang 2010). These conditions create a boundary layer that is warmer than the underlying surface, often leading to a nocturnal inversion layer (Stull, 2017). These conditions lead to a suppression of turbulent mixing within the PBL. Shown in ﬁgure 2.5, the residual CHAPTER 2. BACKGROUND 11 layer (RL) forms a ’cap’ on the underlying nocturnal stable boundary layer. The RL contains pollutants, and moisture from the previous day and more or less retains the atmospheric state of the former CBL. The RL is disconnected from the surface by the SBL and is not affected by turbulent transport form the surface, allowing for turbulence to decay homogeneously in all directions (Liu and Liang, 2010). The RL forms during the evening transition and is eroded in the morning.

2.3 Remote Sensing

This study uses passive remote sensing measurements of atmospheric water vapor to obtain a partial column water vapor product. This section discusses an overview of remote sensing techniques and how these instruments are able to retrieve these products.

2.3.1 Remote Sensing: Overview

By definition, remote sensing is the science of acquiring information about the earth’s surface or surrounding atmosphere without direct contact with targets of interest. This is done by sensing radiation at different wavelengths and processing and analyzing that information, involving the interaction between incident radiation and the target of interest (Canada Centre for Remote Sensing). The Canada Centre for Remote Sensing distinguishes seven elements of the remote sensing process: 1. Energy or Illumination source: The first requirement for remote sensing is to have a source of energy to illuminate the target of interest. This is where CHAPTER 2. BACKGROUND 12 the distinction between passive and active remote sensors are made. The Orbiting Carbon Observatory-2 and the Atmospheric Infrared Sounder are both passive remote sensing systems. 2. Radiation and the Atmosphere: As this energy travels from the source to the instrument, it comes in contact with the atmosphere as it passes through. 3. Interaction with the target: As the energy source (here infrared radiation) passes through the atmosphere, it interacts with the target of interest (atmospheric water vapor molecules) depending on properties of the target and the radiation (bands of absorption). 4. Recording of Energy by the Sensor: After energetic interaction with target, the remote sensor must collect and record the electromagnetic radiation. Here, the abundance of water vapor molecules in the desired column of atmosphere interact with infrared radiation both reflected and emitted by the earth. 5. Transmission, Reception, and Processing: The energy recorded by the sensor must be transmitted to a receiving and processing station where the data are processed further. 6. Interpretation and Analysis: This step incorporates data analysis and visualization techniques. 7. Application: Application of the information we have been able to extract and analyze from the instrument to improve our understanding of our science question and research topic: what I am doing here. CHAPTER 2. BACKGROUND 13

Figure 2.6: Remote sensing of common earth surfaces. Image credit: CRISP

Remote sensing techniques use either active or passive sensors. Passive remote sensing systems measure energy that is naturally available (Canada Centre for Remote Sensing). Passive remote sensing systems measure reflected energy so they are only able to take measurements when this energy is available. Active remote sensing systems provide their own source of energy for illumination of the target. The sensor emits radiation in order to directly measure the desired variables. Lidar and radar remote sensing instruments are examples of active remote sensing systems (NASA EarthData). OCO-2 and AIRS are passive remote sensing sensors. OCO-2 measures in the near-infrared, thus it is only able to make measurements during the day when this energy is available, as this is reflected solar radiation. AIRS measures in the thermal infrared region, which is emitted from the earth day and night. OCO-2 and AIRS are characterized as high spectral resolution hyperspectral satellites. The spectral resolution of an instrument describes the ability of the CHAPTER 2. BACKGROUND 14 sensor to define fine wavelength intervals. The higher (finer) the spectral resolution, narrower the wavelength range within the designated band the instrument is able to see. Thus, higher spectral resolution means the instrument can "see more" atmospheric properties within these infrared wavelength bands. Hyperspectral sensors are advanced multi-spectral sensors. These sensors have the ability to detect hundreds of very narrow spectral bands throughout the designated wavelength bands. The high spectral resolution of these hyperspectral sensors enable them to facilitate fine discrimination between different targets of trace gases and atmospheric properties based on the target’s spectral response in each of the narrow bands. The instruments used here take measurements of atmospheric properties using absorbed infrared radiation by atmospheric molecular water vapor. As radiation is emitted from the earth, it will come in contact with the atmosphere as the signal passes from sensor to earth and back from earth to the sensor. While the signal is traveling between the sensor and the earth, it comes in contact with molecules within that column of atmosphere. The abundancy of these molecules is estimated by the instruments using a diffraction grating to separate the inbound radiation into component wavelength bands. Both instruments utilize wavelengths in which water vapor spectral lines are present, allowing estimates of water vapor column density to be obtained based on the instrument retrieval algorithms (NASA, 2019). CHAPTER 2. BACKGROUND 15

2.3.2 Electromagnetic Radiation

Figure 2.7: The electromagnetic spectrum distinguishes types of radiation based on wavelength. Image credit: ProTherm: Electric Infrared

The ﬁrst requirement of remote sensing of any target is to have an energy source to illuminate the target. Electromagnetic radiation serves as this energy source. By the wave-particle duality property of light, light behaves as both a wave and a particle. In this particular application, light will be considered as a wave. Light waves behave with predictable properties according to the basics of electromagnetic wave theory. The electromagnetic spectrum divides different species of light by ranges of wavelength (or frequency). See ﬁgure 2.7. Wavelength (λ) represents the measured distance between successive wave crests (or troughs) and frequency

1 ( s or Hz) refers to the number of cycles of a wave passing a ﬁxed point per unit of time. The relationship between wavelength and frequency is shown in the below CHAPTER 2. BACKGROUND 16 equation.

c = λv (2.2)

where c = 3.0 ∗ 108 ms−2, the constant of the speed of light. Molecular species interact with light differently depending on wavelength and properties of the molecule. Molecules and free radicals interact with light through either extinction or emission of electromagnetic radiation. Scattering and absorption of electromagnetic radiation being the two means of extinction. These processes will be further explained in subsequent sections.

Figure 2.8: OCO-2 measures in the band of reﬂected sunlight (far left) and AIRS measures in the band of emitted thermal radiation (far right).

Several regions of the electromagnetic spectrum are useful for remote sensing purposes. The passive systems used here use two regions of the infrared spectrum, the near-infrared (shortwave) and the thermal infrared (longwave). Infrared radiation can be divided into two categories based on their radiation properties; the CHAPTER 2. BACKGROUND 17 reﬂected infrared and the thermal infrared (Canada Centre for Remote Sensing, 2019). The reﬂected infrared ranges within the wavelength band of approximately

0.7µm to 4.0µm. OCO-2 spectrometers utilizes this reﬂected, or near-infrared portion of the spectrum. The thermal infrared spectrum, or longwave infrared spectrum, encompasses wavelengths in the range 4.0µm to 100µm. Thermal infrared radiation (red arrow in Figure 2.8) is the radiation that is emitted from the earth’s surface in the form of heat energy. AIRS utilises this portion of the infrared spectrum. As this radiation travels from the source to the sensors, it comes in contact with the surrounding atmosphere as it passes through. Upon this interaction with the atmosphere, measurement of trace gases is made possible through utilization of the diffraction grating within the instruments. The diffraction grating is used to separate incoming light into component wavelengths, thus obtaining the spectral signature of molecules that absorb infrared radiation within the measured bands (NASA, 2018).

2.3.3 Interactions with Radiation

Incoming solar radiation serves as the primary source of electromagnetic radiation for the earth. Before this electromagnetic radiation reaches the earth’s surface, it must interact with the atmosphere as it passes through. These interactions include radiative extinction (in the form of absorption or scattering) and emission. Extinction is a process that decreases the amount of radiant intensity, where emission is a process that increases it. Different molecular species and particles CHAPTER 2. BACKGROUND 18 present in the atmosphere interact with electromagnetic radiation differently. The incoming solar radiation that reaches the earth’s surface is absorbed and re-emitted as longwave thermal radiation (infrared). This process is illustrated in ﬁgure 2.8. The amount of this radiation that gets back to space is controlled by the greenhouse effect; or more formally, absorption of this thermal radiation by principle absorbing gases in this spectrum. Water vapor is a principle absorber of earth’s emitted thermal radiation going back to space, making it a principle greenhouse gas. Water vapor is the most important atmospheric absorber in the infrared band (Petty, 2006). Water vapor’s non-linear molecular structure gives it the ability to make many rotational and vibrational transitions, allowing for a wide absorbance spectrum (Petty, 2006). CHAPTER 2. BACKGROUND 19

Figure 2.9: Atmospheric transmittance. OCO-2 measured spectral bands highlighted in orange, where the far left is the Oxygen-A band, following is the Weak CO2 Band, and the Strong CO2 band. AIRS water vapor band is highlighted in pink. Image credit: Robert A. Rhode for the Global Warming Art project. CHAPTER 2. BACKGROUND 20

2.3.4 Atmospheric Radiative Transfer

The Earth can be considered as a blackbody, meaning it is a perfect absorber and emitter of radiation (Jain, 1991). This simpliﬁes the calculation, as we will see later. The spectral distribution of photon energy for a blackbody can be discribed by the Planck function:

2hc2 B (T) = (2.3) λ λ 5[exp(hc/kTλ) − 1]

Where c is the speed of light, 3 ∗ 108m/s, h is Planck’s constant, 6.62607004 ∗ 10−34[m2kgs−1], and k is the Boltzmann constant, 1.38064852∗10−23[m2kgs−2K−1]. The peak in this spectral distribution is represented by Wien’s Displacement Law:

2898µmK λ = (2.4) peak T

Wien’s Displacement Law shows an inverse relationship between an object’s temperature (T) and the peak in its radiation (λpeak) (Jain, 1991). Thus, as an object’s temperature increases, its peak in radiation spectra occurs at shorter wavelengths. This is shown in ﬁgure 2.10 by displaying the peak in radiating from the Earth and Sun. The Earth has a much lower equivalent blackbody temperature of 288K compared to the Sun’s 6000K, and thus the peak in Earth’s radiation curve occurs at longer wavelengths–the thermal infrared. The Sun’s radiation peaks in the visible region of the infrared spectrum (i.e. higher energy). The peaks in earth and sun radiative spectra are important here, as OCO-2 and AIRS measure in different peaks of these spectra. CHAPTER 2. BACKGROUND 21

Figure 2.10: Planck radiation curves for Sun and Earth. The peak in the sun’s radiation curve occurs in the visible region whereas that of the earth occurs in the thermal infrared region. Image credit: Thomson Higher Education

Kirchhoff’s Law states that emissivity, ε, is equal to absorptivity, α,:

ελ = αλ (2.5)

Where the emissivity of a blackbody is equal to 1 (Jain, 1991). The earth is not a perfect blackbody due to extinction and emission interactions by atmospheric gases. The earth is thus considered a greybody where emissivity is less than 1. For remote sensing purposes, this allows us to extract measurements of gases in select bands of absorption of incoming and outgoing radiation. Radiation passing through the atmosphere can be described by the general CHAPTER 2. BACKGROUND 22 equation of transfer:

dIλ = −βe,λ Iλ ds + βe,λ Jλ ds (2.6)

Where the ﬁrst term of the equation represents radiation lost due to atmospheric extinction processes and the second term represents radiation gained due to emission processes. Electromagnetic radiation passing through the atmosphere going back to space (as measured by satellites) undergoes loss of signal as a result of interaction with atmospheric gases. This interaction is measured by instruments.

Remote Sensing of Water Vapor

How much radiation is received at the top of the atmosphere depends on atmospheric composition as a result of radiative extinction processes. In order to obtain information about atmospheric water vapor, spectral bands of water vapor absorption are chosen.

The 6 to 8 µm spectral band is often referred to as the vibrational-rotational (V-R) band of water vapor, where this band is centered at 6.7 µm (Bruke et al., 2006). This thermal infrared region is the spectral region dominated by water vapor absorbance. The AIRS instrument measures in this band. This means the primary gas contributing to radiative transfer to the top of atmosphere is water vapor. Figure

2.10 shows that the peak in earth’s longwave radiation occurs at around 10 µm. The proximity of the peak in earth’s emitted radiation and the center of water vapor CHAPTER 2. BACKGROUND 23 absorbance further displays its importance to the greenhouse effect. Figure 2.11 shows the water vapor transmittance spectra. The 6 to 8 µm band is the principle band of water vapor absorption; however, there are many spectral regions were water vapor absorption occurs. Water vapor lines occur where other gases are also principle absorbers, allowing numerous algorithms to retrieve a water vapor product, as seen with OCO-2 and further explained in section 3.1.2.

Figure 2.11: Water Vapor transmittance spectra. Image credit: Leinweber, 2010)

The Atmospheric Infrared Sounder (AIRS) takes soundings of atmospheric water vapor, meaning these are measurements based on height. Hyperspectral sensors in satellites allow for direct investigation of water vapor vibrational-rotational states, therefore enabling proﬁle retrieval. Hyperspectral sensors measure a range of wavelengths. The purpose of this is to obtain information from different regions of the atmosphere. The sensitivity of the instrument is dependent on the rate of change of transmittance t(z) with height, z, or more formally labeled as the weighting function W(z) (Petty, 2006). CHAPTER 2. BACKGROUND 24

The optical depth τv describes how much absorption occurs when light travels through a medium: Z τv = kvρw(z)dz (2.7)

Where kv represents the water vapor mass-absorption coefficient and ρw(z) represents the water vapor number density (mass concentration [kgm−3] (Bruke et al., 2006). The water vapor profile is the vertical change of water vapor mass concentration with height, z. Water vapor emission/absorption increases directly with mass concentration. Since water vapor is the dominating absorbing species in this region, its vertical profile largely determines the transmittance of the atmospheric column in the

6 − 8µm spectral region (Bruke et al., 2006). Transmittance describes how much radiant energy passes through a given region and can be mathematically described as the exponential of optical thickness:

−τv tv = e (2.8)

Thus when τv = 1, tv = 0 representing complete absorbance, and when τv = 0, tv = 1 absorbance is completely absent, as absorptivity is equal to:

α = 1 −tv (2.9)

The change in transmittance, tv, with height, z, is thus related to the change in absorbance per unit altitude (Petty, 2006). This quantity is dependant on water CHAPTER 2. BACKGROUND 25

vapor mass concentration, ρw(z), the water vapor mass-absorption coefﬁcient, kv, and height z (Bruke et al., 2006).

dt (z) W(z) = v (2.10) dz

The shape of the weighting function is determined by two factors: the decrease in absorbing gas concentration with height (i.e. water vapor concentration decreases with height), and the increase in transmittance with height. The peak in the absorption weighting function occurs at height, z, for which optical depth, τv = 1 (Petty, 2006).

dt Figure 2.12: The relationship between the weighting function dz , transmission, t(z), and gas proﬁle, ρ(z) Image cregit: Petty, 2006 ρ0 CHAPTER 2. BACKGROUND 26

The weighting function indicates the region within the atmospheric column where most of the absorption is coming from (Bruke et al, 2006). This occurs at different regions in the atmospheric column depending on water vapor mass concentration and wavelength used. Different wavelengths will obtain different peaks in absorption within the atmosphere, enabling retrieval of information across various atmospheric heights. The weighting function is nearly zero at high altitudes where the atmospheric density is small and it is also zero near the surface where transmission is zero, or below the maximum depth to which radiation can penetrate (Petty, 2006). The maximum value is reached somewhere between the top of atmosphere and surface, where tv(z) is changing most rapidly (Petty, 2006). The altitude of peak absorption is dependant on the strength of absorption, described by the absorption coefficient, kv. This is wavelength-dependant, so the altitude of peak absorption changes as wavelength does. Provided that kv and the vertical temperature profile, T(z), are known, it is possible to retrieve the vertical concentration of absorbing/emitting species as a function of vertical location, i.e. an atmospheric water vapor sounding (Bruke et al., 2006). As the number of measurement bands increases, so does the accuracy and vertical resolution of the retrieved vertical mass-concentration (Bruke, et al., 2006). Simply: using more channels results in better water vapor profiles. This principle and the vertical sensitivity (weighting functions) of the instruments used in this study were the motivation behind the differencing method described in chapter 4. 27

3. Data sets

Figure 3.1: Purple rectangle shows the extent of the satellite data with dimensions -98.0, -97.38, 36.25, 37.25 (WESN) used in the SGP validation. Yellow star shows location of ARM SGP site used for validation. Plotted over MODIS data from November 26, 2019.

The level 2 B9 Lite OCO-2 files and the level 2 version 6.2 support products from AIRS were used in this study. NASA’s Earth Observing System Data and Infor- mation System (EOSDIS) data products are processed at different levels ranging from Level 0 to Level 4 (NASA 2019). Level 0 data products are raw, unprocessed CHAPTER 3. DATA SETS 28 payload data with full instrument resolution. Level 1A data products are still unprocessed data with full instrument resolution but have been time-referenced and annotated with calibration coefficients and georeferencing parameters. Level 1B products have been processed to sensor units. Level 2 data products have derived geophysical variables at same resolution and location as level 1 data. Level 3 products contain variables that have been mapped on uniform space-time grid scales, usually with some completeness and consistency. Some instruments have level 4 data products that model output or results from analyses of lower-level data (NASA 2019). The extent of the satellite data used in this study is shown in figure 3.1 by the purple box. The geographic coordinates of the box are -98.0, -97.38, 36.25, 37.25 where the ARM SGP site used for the first validation is located at 36.605, -97.45167. Vaisala RS92 radiosondes were used for validation purposes in this study. Vaisala R292 radiosondes are the most widely used type of radiosonde within the global radiosonde network (Wang et al., 2013). Global radiosonde data are historically an important resource for understanding atmospheric thermodynamic state and climate change. Radiosonde data provide the longest measurement record of upper-air temperatures, humidity, and winds with near-global (land) coverage and high vertical resolution. The long record of radiosonde data and high credibility of measurements make radiosonde data important sources for purposes of satellite validation. The validation site used in this study has a long record of historically credible data and reoccurring satellite overpasses, making ground-based measurements at the Southern Great Planes site an excellent source CHAPTER 3. DATA SETS 29 for instrument validation.

3.1 The Orbiting Carbon Observatory-2

The Orbiting Carbon Observatory-2 was launched in May 2014 with the in- tention of quantifying atmospheric carbon dioxide from space with enough precision to quantify its regional sinks and sources (Nelson et al., 2016). OCO- 2 uses hyper-spectral observations of near-infrared reflected sunlight from the earth’s surface to measure abun- Figure 3.2: OCO-2 measurement of atmospheric carbon dioxide. Image dance of trace gases within the given credit: NASA/JPL atmospheric column. Measurements from OCO-2 are only obtainable with clear-sky conditions because reflected sunlight must be able to reach the instrument. Presence of clouds or dense aerosols often induce large errors in retrieval due to atmospheric scattering. OCO-2 takes eight measurements simultaneously across a 10 km swath every 2.29 km along its track with footprint dimensions of 1.5 km x 2.29 km (Nelson et al., 2016). The satellite flies in a sun-synchronous polar orbit crossing the equator at approximately 13:30 local time every day achieving near-global coverage of the sun-light portion of the earth with a 16 day repeat cycle (NASA, 2019). See figure 3.3 for a CHAPTER 3. DATA SETS 30 visualization of the daily global extent of OCO-2 measurements.

Figure 3.3: Global path of OCO-2 for 06-04-2019. Nadir mode observations are shown in blue and glint mode observations are shown in yellow. Image credit: NASA/JPL

3.1.1 Modes of Operation

OCO-2 has three modes of operation: nadir, glint, and target mode. Multiple operation modes increases data quality and verifies validity of mission data (NASA, 2019). In nadir mode, the satellite points the instrument straight down to the local nadir, so that data can be collected along the instrument’s ground track below the spacecraft (NASA, 2019). Nadir mode observations provide the highest spatial resolution over land; however, is not optimal over the oceans. Glint mode is used over the ocean by significantly improving the instruments signal to noise ratio over dark surfaces. When operating in glint mode, the satellite points the instrument towards the bright "glint" spot, where visible light from the sun is reflected from the surface (NASA, 2019). The entire globe is mapped in each mode every 32 days as the satellite alternates between nadir and glint mode on 16-day ground track repeat cycles (NASA, 2019). The instrument occasionally operates in target mode CHAPTER 3. DATA SETS 31 where it will lock its view onto a specific location on earth’s surface and retains that view while flying overhead (NASA, 2019). Target mode observations are acquired over an OCO-2 validation site roughly once per day (NASA, 2015). The target observation lasts up to 9 minutes. Over the period of overpass, the instrument can collect over 12,000 samples of the desired variable. See figure 3.4 for a visual representation of the three modes of operation of OCO-2. The OCO-2 data used to validate the ARM SGP site was taken using the target observation mode, this resulted in over 4,000 separate measurements for the total column water vapor product. In order to carry out this study, the target observations of total column water vapor product were averaged and that average was used in the final analysis.

Figure 3.4: Observation modes of OCO-2. Image credit: NASA/JPL

Figure 3.5: Target mode observation path of OCO-2 for 06-04-2018. target mode observations are shown by the red dot. The purple line shows the extent of ﬂight instrument was locked on the target. Image credit: NASA/JPL CHAPTER 3. DATA SETS 32

3.1.2 Measurements

The OCO-2 satellite does not directly measure atmospheric carbon dioxide or water vapor, but it measures the intensity of reflected sunlight off the earth’s surface from specific geographic locations. As explained in the background section, gas molecules in the atmosphere absorb light at specific wavelengths resulting in unique spectral fingerprints that are measured by the spacecraft’s instrument. The absorption levels found in these measured spectra show the abundance of specific gases in the observed column of atmosphere.

Figure 3.6: Wavelength bands for O2 and CO2. Image credit: NASA/JPL

OCO-2 measures near-infrared light in three spectral bands centered at about

0.76, 1.61, and 2.06 µm (Nelson et al., 2016). The Oxygen A-Band wavelength channel is centered at 0.76. The Oxygen A-Band spectra indicates the presence of clouds and optically thick aerosols that have the potential to obstruct the instrument’s measurement (NASA, 2019). Observations from this band are addi- tionally used to infer atmospheric pressure and solar light path length as sunlight passes through the atmosphere (NASA, 2019). The weak CO2 band is centered at wavelength 1.61 µm (Nelson et al., 2016). The weak CO2 band has the highest near-surface sensitivity for atmospheric CO2. The strong CO2 band is centered CHAPTER 3. DATA SETS 33

at 2.06µm (Nelson et al., 2016). The strong CO2 band spectra is sensitive to the presence of atmospheric aerosols, pressure, and humidity (NASA, 2019). The instrument uses a diffraction grating to separate incoming radiation into a spectrum of component wavelengths; because water vapor molecules absorb light energy within the measured spectral bands, the instrument is able to retrieve a total column water vapor product representing the total gaseous water contained in the measured vertical column of atmosphere (Nelson et al., 2016).

Figure 3.7: Spectral reﬂectance of common earth surfaces in the three measured bands of OCO-2 Image credit: NASA/JPL

3.1.3 Total Column Water Vapor Product

There are many water vapor spectral lines present in the the near-infrared wavelengths observed by OCO-2 (Nelson et al., 2016). Water vapor produces measur- able absorption in all three of these bands bands, making it the most important CHAPTER 3. DATA SETS 34 interfering gas in retrieval (Crisp et al., 2015). The retrieval algorithm used for

XCO2 retrieval is the Atmospheric Carbon Observations from Space (ACOS) algorithm (Nelson et al., 2016). ACOS uses optimal estimation to retrieve the column averaged dry-air mole fraction of CO2 (XCO2 ) and numerous other quantities of atmospheric variables that affect the retrieval of XCO2 (Nelson et al., 2016). Errors are introduced into the XCO2 product when there are H2O absorption lines unac- counted for, so one of the retrieval products within the ACOS algorithm is total column water vapor (TCWV) (Nelson et al., 2016). Upon validating boundary layer water vapor in this study, it was necessary to also validate the integrated total column water vapor from each instrument. Figure 3.8 shows that OCO-2 quantiﬁes total column atmospheric water vapor with extremely high accuracy. Figure 3.8 has a correlation coefﬁcient of 0.99 when compared against the ARM SGP validation site. This data set contains all of the target mode data from each day matched with OCO-2 overpasses. The spread of the target data is shown by the spread of the points on the below plot. CHAPTER 3. DATA SETS 35

Figure 3.8: Validation of OCO-2 total column water vapor yields a correlation coefﬁcient of 0.998 against the SGP validation site. CHAPTER 3. DATA SETS 36

3.2 The Atmospheric Infrared Sounder

The Atmospheric Infrared Sounder (AIRS) is an advanced infrared sounding system and is one of six instruments aboard NASA’s Earth-Observing System Aqua. Aqua was launched on May 4, 2002 and follows the same sun-synchronous polar orbital path as

OCO-2 as a part of NASA’s "after- Figure 3.9: Comparison of AIRS and OCO-2 footprints. AIRS has swath noon constellation", or A-train, of earth- dimensions of 2250 km x 1650 km and observing satellites (NASA, 2019). The OCO-2 takes measurements across a 10 km swath every 2.29 km. Image credit: satellites in the A-train cross the equa- Amy Braverman at NASA/JPL tor at 13:30 local time daily. The objective of the AIRS mission is to support climate research and improve weather forecasting by observing global water and energy cycles, climate varia- tion and trends, and the climate system’s response to an inﬂux of anthropogenic greenhouse gases (NASA 2019). The instrument has the ability to cover up to 95 percent of earth’s surface on any given day, taking measurements in a 3x3 array of observations over 2250 km x 1650 km swaths (NASA, 2019). See ﬁgure 3.9 for comparison of AIRS and OCO-2 swaths. The spatial resolution of AIRS is on the order of 15 km at nadir and is coarse compared to that of MODIS (on the order of 1 km) (Susskind, 2006). AIRS measures select variables as a function of CHAPTER 3. DATA SETS 37 height in the atmosphere; resulting in soundings of measured variables through the atmospheric column.

3.2.1 Measurements

AIRS is a high resolution infrared spectrometer with 2,378 spectral channels cover- ing the range 650cm−1 to 2675 cm−1 (Susskind, 2006). This includes wavelength ranges 3.74 − 4.61µm, 6.20 − 8.22µm, and 8.8 − 15.4µm (Divakarla et al., 2006). The 6.20 − 8.22µm range is used for water vapor retrieval, as the principle line of water vapor absorption is centerd at 6.7µm (Susskind, 2006). AIRS has a ﬁeld-of-view of 1.1◦ and has a spatial resolution of 13.5 km, and nominal spectral resolution of λ/∆λ = 1200 (Aumann et al., 2000). The majority of these channels are opaque, meaning these are channels of atmospheric absorption. Opaque channels are useful for observing atmospheric temperature and proﬁles of select energy-absorbing gases (Susskind, 2006).

3.2.2 Integrated Total Column Water Vapor

AIRS measures profiles of water vapor at 15 different pressure levels at 14 different layers of atmosphere. The instrument takes numerous different measurements relating to atmospheric water vapor, including different levels of support products and quality flags. The water vapor retrieval product specifically used here describes the water vapor profile within the measured layer of atmosphere in [kg/m3]. In order to carry out this study AIRS measured upper column perceptible water vapor CHAPTER 3. DATA SETS 38 was integrated with the lower limit being the top of the planetary boundary layer (PBLH), in order to get this in units of [kg/m2]:

Z atmtop columnupper = nxdz (3.1) PBLH

This was also done for the total column where results against the validation site are shown in ﬁgure 3.10. The earth’s surface served as the lower limit for the integration.

Figure 3.10: Validation of AIRS total column water vapor yields a correlation coefﬁcient of 0.96 against the SGP validation site. CHAPTER 3. DATA SETS 39

3.3 Water Vapor Number Density

One of the most important applications of the molecular integrated column number density is to measure the absorption or scattering of light by an optically active gas (i.e. water vapor). The degree of absorption or scattering of light is dependant on the number of molecules in the path of the beam of light, and therefore the column number density (Harvard, 2018). The water vapor integrated column number density is a quantiﬁcation of the number of molecules of gas X (i.e., water vapor) per unit volume of air. The integral over a given depth of atmosphere deﬁnes the atmospheric column X in equation 2.2:

Z z column = nxdz (3.2) z0

where z0 is the surface and z is the height at the top of the column. The term nxdz is the molecular number density with change in height, z. The mixing ratio of a gas is deﬁned as the number of moles of molecule in question per mole of air. The mixing ratio (Cx) and the mixing ratio of a gas (nx) are related by the number density of air (na) shown by equation 2.3 :

nx = Cxna (3.3)

Thus the number density of a gas is related to atmospheric pressure P by the ideal gas law. Consider a volume of air, V, at pressure P with temperature T CHAPTER 3. DATA SETS 40 containing N moles of air. Where R = 8.31 Jmol−1K−1:

PV = NRT (3.4)

The number density, nx, is related to N and V by equation 3.5 where Av is

Avogadro’s number, Av = 6.023E23 molecules per mole:

A N n = v (3.5) 0 V

Substituting equation 3.5 into equation 3.4, we obtain:

A P n = v (3.6) 0 RT

and thus column number density can be calculated using inputs available from radiosonde data:

A P n = v C (3.7) x RT x

3.4 ARM Mergesonding Value-Added Product

The partial column water vapor products calculated via the above differencing formula were validated using ARM facilities mergesonding products. The merged sounding value-added product uses combined observations from radio soundings, microwave radiometers, surface meteorological instruments, and European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs, coupled with a CHAPTER 3. DATA SETS 41 sophisticated interpolation/smoothing scheme to define profiles describing atmospheric thermodynamic state at one-minute temporal resolution at 266 different altitude levels (Troyan, 2012). The mergesonding product produces a thermodynamic profile of the atmosphere that is created by building two temporary profiles on the same grid. The first profile is comprised of one 24-hour profile created using only radiosonde data and interpolation techniques. The second profile is created using ECMWF data and the same interpolation techniques. The two profiles are then "merged" into one thermodynamic profile using the time from the nearest radiosonde observation to determine the weight that each profile contributes to the combined mergesonding product (Troyan, 2012). 42

4. Methodology

In order to quantify water vapor in the planetary boundary layer, this study com- bines water vapor products from the Orbiting Carbon Observatory-2 (OCO-2) and the Atmospheric Infrared Sounder (AIRS) using a differencing method described in the following section.

4.1 Partial Column Water Vapor Calculation

The AIRS calculated integrated upper column water vapor product was subtracted from the OCO-2 total column water vapor product in order to get a product of partial column water vapor in the planetary boundary layer. This method is simply outlined in figure 4.1. The total column water vapor product derived by the Orbiting Carbon Observatory- 2 represents the total abundance of water vapor molecules in a given column of atmosphere. This product is not separated based on height (i.e. it is not a profile). The product has excellent sensitivity throughout the atmospheric column, shown by the instrument weighting function and further by the 0.99 coefficient of correlation CHAPTER 4. METHODOLOGY 43 in total column water vapor validation in figure 3.8. The weighting function (or averaging kernel) displays that the instrument is equally sensitive throughout the measured column of atmosphere. Thus, OCO-2 has equal sensitivity in the lower and upper atmosphere. The AIRS instrument retrieves profiles of atmospheric water vapor based on 14 different atmospheric layers, as described in section 3.2.2. The general process in which AIRS retrieves profiles of atmospheric water vapor are outlined in section 2.4.3. The water vapor profiles retrieved by the AIRS instrument have corresponding pressure (or height) levels, making it possible to obtain a partial column water vapor product in the upper troposphere by integrating the water vapor profile using the top of the boundary layer as the lower bound.

This upper-column water vapor product obtained from the AIRS proﬁle (UCWVAIRS) was then subtracted from the total column water vapor product obtained from OCO-

2 (TCWVOCO−2) to get a partial column water vapor product in the planetary boundary layer (PCWVPBL):

PCWVPBL = TCWVOCO−2 −UCWVAIRS (4.1) CHAPTER 4. METHODOLOGY 44

Figure 4.1: Integrated AIRS upper column water vapor in red is subtracted from OCO-2 TCWV in orange to get the integrated partial column water vapor in the planetary boundary layer, in green. Where the number density proﬁle in units molecules/cm3 of water vapor is plotted in grey and the boundary layer top is in purple.

4.2 Boundary Layer Height

4.2.1 Bulk Richardson Number Method

Quantiﬁcation of the height of the boundary layer was vital for the purpose of this study, as the top of the boundary layer served as the lower bound for AIRS upper column calculation. The Bulk Richardson Number Method was used to calculate the height of the planetary boundary layer. The Bulk Richardson number method is often used for the purpose of numerical weather prediction and climate models due to its reliability under a variety of atmospheric conditions (Zhang et al., 2014). CHAPTER 4. METHODOLOGY 45

The Bulk Richardson Number (BRN) method assumes that the interface between the free troposphere and boundary layer occurs at the point in the atmospheric column where turbulence halts and an air parcel becomes neutrally buoyant. The BRN method is based off of the coefficient relating buoyancy and turbulence (the Bulk Richardson Number); this method of calculation uses the variability of the Bulk Richardson Number (BRN) within the boundary layer to quantify height and assumes that the height of the boundary layer is equivalent to the height at which the BRN reaches a threshold value (Zhang et al., 2014). The BRN is a dimensionless ratio relating the convective available potential energy (buoyancy) to vertical wind shear (turbulence). The BRN at certain height z, is calculated using the below equation: g ( )(θVz − θV0 )z θV0 Rib = 2 2 (4.2) uz + vz where θV0 and θVz are the virtual potential temperatures at the surface and at height g 2 2 z, respectively, is the buoyancy parameter, and uz and vz are the horizontal wind θV0 components at height z (Zhang et al., 2014). The biggest challenge in using this method to calculate boundary layer height is the fact that the critical BRN value must be determined as a prior known. The value of BRN depends on the current state of atmospheric stability. In general, the value of the BRN increases as the atmosphere becomes more unstable. The height of the PBL is denoted as the point at which the calculated Bulk Richardson number is less than the defined critical BRN. The critical BRN ranges from 0.23-0.50 depending on thermodynamic stability, with the highest values of BRN observed in unstable boundary layers. For CHAPTER 4. METHODOLOGY 46 the purpose of this study, a generally-accepted critical BRN of 0.25 was used. This method was used to calculate the boundary layer height using inputted variables from the mergesonding products. The Bulk Richardson number is calculated at every given height level until it reaches the critical value of 0.25; the point at which the calculated BRN reaches this critical value is the boundary layer height. In this study, this calculated value was used for both the validation and satellite- based calculation. The AIRS instrument has a boundary layer height product within its measured variables; however, these products were not used in this study because they were flagged with poor quality by the boundary layer height quality check. As a result, the boundary layer heights calculated using inputs from the radiosonde data were also used in the satellite-based partial column calculation. Consistency of the boundary layer height between the two data sets allowed for full comparison of partial column water vapor products.

4.3 Validation

To validate the satellite-based partial column water vapor using the above method, mergesonding products from matched dates and times were used. The one-minute temporal resolution of the mergesonding product allowed for exact date and time matches with corresponding OCO-2 overpasses to maximize validation purposes. For the validation, there was a small "box" drawn around the SGP site with dimensions of +36.25 to +37.25 degrees latitude by -98.0 to -97.38 degrees longitude CHAPTER 4. METHODOLOGY 47 in order to obtain matching satellite overpasses within this "box" (this is approximately equal to 124 km). See Figure 3.1 for a visualization of this box. The chosen coincidence criteria had to be a relatively small area due to the high variability of water vapor with space and time. The merged sounding proﬁle corresponding to the exact time of OCO-2 overpasses were used, which typically occurred around 13:30 local time. As a result of water vapor’s variability with space and time, matching the validation proﬁle to the minute was critical for the purpose of this study. 48

5. Results and Discussion

5.1 Results

The calculation of water vapor in the planetary boundary layer using the above differencing method yielded a coefﬁcient of correlation of 0.98 when validated against the mergesonding product.

Figure 5.1: Validation of combined OCO-2 and AIRS partial column water vapor product in the planetary boundary layer. Correlation coefﬁcient of 0.98. CHAPTER 5. RESULTS AND DISCUSSION 49

Dates OCO-2-AIRS PBL kg/m2 MS PBL kg/m2 AIRS PBL kg/m2 PBL height m 20141003 4.99 4.88 4.53 1598 20141030 7.71 6.93 6.20 1938 20141108 4.44 3.88 3.54 1238 20141124 4.32 4.06 4.22 2318 20141210 5.20 3.05 1.37 758 20150210 0.61 0.84 1.90 838 20150219 2.91 1.63 2.59 1058 20150305 1.85 2.17 2.62 1698 20150517 11.05 10.77 6.06 1278 20150620 14.06 14.04 15.23 1458

Table 5.1: Boundary layer water vapor. Where OCO-2-AIRS PBL shows the calculated boundary layer water vapor, MS PBL shows the partial column water vapor calculated from the mergesonding data, AIRS PBL is the partial column water vapor calculated just using AIRS, and PBL height is the height of the boundary layer top in meters, calculated using the Bulk Richardson Number Method. All numbers have been rounded to 2 decimal places.

Dates OCO-2 TCWV MS TCWV AIRS TCWV AIRS UCWV 20141003 10.80 10.68 10.34 5.81 20141030 10.78 10.89 9.27 3.07 20141108 8.13 8.96 7.23 3.69 20141124 5.38 5.68 5.28 1.06 20141210 17.07 17.09 13.24 11.87 20150210 12.14 12.82 13.43 11.53 20150219 9.29 9.35 8.97 6.38 20150305 3.87 3.54 4.64 2.02 20150517 16.62 16.37 11.64 5.58 20150620 32.67 31.68 33.82 18.61

Table 5.2: Total column water vapor from OCO-2, the Mergesonding data (MS), and AIRS. AIRS Upper column water vapor (UCWV) is also shown. All reported numbers have been rounded to 2 decimal places and are in units of kg/m2. CHAPTER 5. RESULTS AND DISCUSSION 50

Dates OCO-2 TCWV Mean kg/m2 TCWV Variance TCWV Standard Deviation 20141003 10.80 0.13 0.37 20141030 10.78 0.07 0.26 20141108 8.13 0.16 0.40 20141124 5.38 0.04 0.19 20141210 17.07 0.11 0.33 20150210 12.14 0.02 0.14 20150219 9.29 0.01 0.09 20150305 3.87 0.01 0.09 20150517 16.62 0.11 0.34 20150620 32.67 0.34 0.58

Table 5.3: Total column water vapor from OCO-2 was taken in target mode. Target data was averaged for main analysis. All reported numbers have been rounded to 2 decimal places.

In order to do the main analysis of this study, it was ﬁrst necessary to compare the total column water vapor products of the two instruments against the total column water vapor product calculated from the radiosonde data. These results are discussed in further detail in the data sets section, but in short, OCO-2 estimates total column water vapor with 0.99 correlation when compared to the radiosonde data and AIRS agrees with a 0.96 coefﬁcient of correlation. The slightly lower agreement of the AIRS total column can be attributed to the degradation in AIRS sensitivity in the lower atmosphere (i.e. the boundary layer) that will be discussed below. The total column water vapor products from OCO-2 used in the main analysis were all taken in target mode. (See section 3.1.1 for a further explanation of target mode). The ARM SGP site is a common location for instrument validation purposes because of the long historic record of ground-based measurements. The CHAPTER 5. RESULTS AND DISCUSSION 51

Orbiting-Carbon Observatory-2 often makes observations in target mode at this location, resulting in hundreds-to-thousands data products for a given day. For simple analysis, these measurements were averaged. The mean and standard deviation of these points are summarized in table 5.3. Though averages of OCO-2 target points for each of the ten days were used in the main analysis, they have a mean standard deviation of 0.27909915. This mean standard deviation between daily points is not signiﬁcant to skew results of the ﬁnal analysis.

5.2 Discussion

The method of obtaining a partial column water vapor product in the boundary layer used in this study utilizes shortwave OCO-2 measurements of water vapor for the boundary layer. The retrieval algorithm and three wavelength bands used by OCO-2 does not enable the instrument to separate water vapor measurements based on height. Retrieval products from the AIRS instrument are thus used to obtain a product of upper column water vapor to subtract from the total column obtained by OCO-2, giving an estimate of boundary layer water vapor in the shortwave infrared. Based on figure 5.1, using the shortwave infrared bands measured by OCO-2 estimate boundary layer water vapor with high correlation when validated against radiosonde data at the SGP site. To further investigate these results, a similar analysis was done just using AIRS measurements in the longwave infrared. AIRS profiles were integrated from the surface to the top of the boundary layer to obtain CHAPTER 5. RESULTS AND DISCUSSION 52 an AIRS partial column water vapor product in the boundary layer. This yielded a coefficient of correlation of 0.91 when compared to the boundary layer water vapor obtained from radiosonde data. Further shown below in figure 5.2:

Figure 5.2: using only AIRS to quantify boundary layer water vapor yields a correlation coefﬁcient of 0.91.

The weighting function shows the altitude of peak absorption of constituent gas. The AIRS instrument averaging kernel (weighting function) for water vapor is shown in ﬁgure 5.3. The peak in average instrument sensitivity to water vapor occurs in the upper-to-mid troposphere, as shown by the red peak in ﬁgure 5.3. The instrument sensitivity to water vapor retrieval degrades past this point, thus showing lower sensitivity in the lower atmosphere, or boundary layer. Conversely, the blue line shows that the sensitivity of OCO-2 retrieval does not degrade in the boundary layer. This is thus consistent with the above results. CHAPTER 5. RESULTS AND DISCUSSION 53

Figure 5.3: AIRS sensitivity peaks in the upper-to-mid troposphere (Red line), OCO-2 is sensitive throughout atmospheric column. Image credit: original image by Amy Braverman (JPL), cropped and edited by me. 54

6. Conclusions

Conclusion

The method of obtaining a partial column water vapor product from OCO-2 by subtracting AIRS upper column water vapor yielded high correlation with the derived radiosonde product. Accurate knowledge of atmospheric water vapor is an important input for numerical weather prediction and for understanding of earth’s radiative feedback system. Quantiﬁcation of atmospheric water vapor has a long history, both using ground-based and space-based methods. In order to fully understand climate change and future changes to the earth’s radiative energy balance, accurate knowledge of water vapor is necessary. The majority of atmospheric water vapor resides in the lower troposphere, making accurate quantiﬁcation of water vapor in the planetary boundary layer extremely important. This study has shown that combining water vapor measurements from two regions of the infrared spectrum gives us the ability to estimate water vapor in the lower troposphere with very good agreement when compared to historically-accurate ground-based radiosonde data. CHAPTER 6. CONCLUSIONS 55

Space-based boundary layer water vapor quantiﬁcation has the potential to greatly improve spatial and temporal resolution of boundary layer water vapor measurements, signiﬁcantly improving numerical weather prediction and forecasting. Water vapor is the principle regulator of short-term weather system development and cloud formation, making knowledge of lower tropospheric water vapor vital for meteorological knowledge. Water vapor’s high capacity for radiative feedback (attribution to the greenhouse affect) make accurate knowledge of atmospheric water vapor crucial for understanding earth’s changing climate.

6.1 Future Work

Further analysis must be done in order to expand this study to a global scale. The data was temporally limiting, spanning from 2014 October to 2015 June, as OCO-2 data is available after October 2014 and the ARM Mergesonding data set stopped in June 2015. I am doing further analysis in different locations and times to further validate this method using IGRA radiosonde data in Eastern Europe and Africa. 56

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