Geophysical laboratory I / II (Academic year 2025/2025)

Rain drops are formed through coalescence of smaller droplets, which is a consequence of collisions between droplets. Student task will be to model the collision-coalescence process using a probabilistic description. The goal is to quantify the number of "lucky" droplets, which are droplets that undergo a series of unlikely collisions and grow to much larger sizes than average droplets. Results will be compared with theoretical estimates. This exercise is intended as a follow-up to the exercise "Simple model of collision-coalescence in clouds".

Students will learn how to run and analyze numerical simulations of clouds. Simulations will be done using the University of Warsaw Lagrangian Cloud Model, a state-of-the-art model developed at IGF. Data analysis will make use of the Xarray Python package. Both simulations and data analysis will be done through a Jupyter Notebook ran on a supercomputing cluster. Basic knowledge of Python programming language is the only prerequisite for this laboratory.

The aim of the exercise is to analyse properties of biomass-burning aerosol. The goal is to derive profiles of aerosol optical properties, depolarization ratio and relative humidity, so as to characterize the atmosphere using the signals of ADR-PollyXT lidar and NARLa lidar. The student will use available lidar observations in combination with weather profiling of radiosounding and photometric measurements. The data will be processed using available numerical programs, including estimates of measurement errors. For the analysis and interpretation of the processed data, the student will use the methodology proposed by him/herself.

The rate of sublimation is commonly calculated using simple Hertz-Knudsen equation. This equation was derived ignoring microstructure of material and assuming equilibrium distribution of the velocities of molecules condensing on the surface and leaving it. Thus, is it gives only approximate result. It can be corrected using temperature dependent sublimation coefficient (e.g. Kossacki et al. 1999; Gundlach et al. 2011; Kossacki et al. 2017).  

Exercise: Sublimation of ice is investigated in laboratory, using cooled vacuum chamber. Measured parameters are: position of the surface and the temperature. Student is expected to perform measurement and derive the temperature dependent rate of sublimation.

This exercise is dedicated to advanced student.

Note that, due to the COVID-19 situation, the student will receive raw measurement data for analysis.

The exercise concerns development of simple version of an eye-safety lidar simulator. The task of the student is to design the methodology and come up with an algorithm for calculation of the eye-safety for a lidar with pulsed laser and vertically-aiming beam with respect to the overflying aircraft. The developed code should have several crucial parameters settable by user. The student need to decide which parameters must be considered, whereby both laser and aircraft characteristics need to be take into account in this case. As part of the report, student need to provide the guidelines on how to use the code.

The exercise is aimed at determination of the thermal conductivity of granular ice, or natural snow (if it is available) without sampling the test material. The measurement is made using linear probe technology. It is used in practice in situations when taking a sample of the material is inexpedient or technically impossible. This method is applied to investigate directly (in-situ) properties of cosmic bodies using automatic landing probes, e.g. comet Churyumov-Gerasimenko (mission Rosetta, experiment MUPUS).

Idea is the following: changes of the temperature of a long thin heater inserted in a solid material is a function of its thermal conductivity. When the heating power is known it is sufficient to register  changes of the temperature. The latter can be done automatically. 

Student is expected to perform 2 -3 measurements and analyze the source data. 

This exercize is dedicated to advanced student.

Note that, due to the COVID-19 situation, the student will receive raw measurement data for analysis.

Turbulence kinetic energy, dissipation rate and the integral length scale
are the basic physical quantities which characterize turbulence. They are used in various
turbulence models and parametrization schemes.
Within this exercise a student will analyze wind velocity data measured
during EUREC4A campaign and estimate the above mentioned quantities.
The student will investigate whether the Taylor law, which is a classical
relation between these three basic quantities is satisfied.

The aim of the exercise is to derive properties of minerat dust agigination if African or Asian deserts. The profiles of aerosol optical properties, depolarization ratio and relative humidity will be obtained, so as to characterize the atmosphere using the signals of ADR-PollyXT lidar and NARLa lidar. The student will use available lidar observations in combination with weather profiling of radiosounding and photometric measurements. The data will be processed using available numerical programs, including estimates of measurement errors. For the analysis and interpretation of the processed data, the student will use the methodology proposed by him/herself.

The exercise is to define a methodology for synergistic data analysis of the ESA EMORAL lidar and the LATMOS BASTA cloud radar profiles to predict the tempo-spatial distribution of aerosol and clouds in the atmosphere. The key issue here will be (a) correctly calibrating the measurements taken by both devices and b) setting and optimising signal thresholds to distinguish different types of aerosol and clouds.

NOTE: This topic is open for realization only for students who already have experience with atmospheric physics!

The exercise aims at familiarizing students with the basic microphysical properties of clouds (concentration and size of cloud droplets), their variability in space, and their dependence on the type of cloud. The exercise will involve the analysis of the measurement data from the ACE2 (Second Aerosol Characterization Experiment, Canary Islands) and the RICO (Rain in Cumulus over the Ocean; Caribbean, 2004-2005) experiments carried out in Stratocumulus and Cumulus clouds, respectively. The implementation of this exercise will allow students to effectively learn the basic (and more advanced) parameters characterizing clouds, understand and remember which are the most important processes that govern clouds.

Aethalometer and Polar nephelometer are used to measure aerosol absorption and scattering coefficients. In case of both devices, due to the measurement methodology, determination of such quantities requires applying a series of corrections. As a part of the exercise, student will write the software to derive the single scattering albedo. The method is to be applied in urban polluted (Warsaw) and peri-urban (Vilnius) conditions.

The aim of the exercise is to estimate the aerosol radiation forcing on the basis of observation of surface solar flux and radiative transfer simulation of aerosol-free solar fluxes. In addition, the radiation budget at Earth’s surface will be determined, as well as the total energy budget, including sensible and latent heat fluxes.

The aim of the exercise is to derive the profiles of the aerosol depolarization (UV and VIS), water vapour mixing ratio, and fluorescence efficiency from the European Space Agency Mobile Aerosol Raman EMORAL lidar signals. Student will use lidar measurements for different cases, e.g. Rayleigh atmosphere, air-mass of biomass combustion, and air mass of mineral dust. She/He will write numerical programs or use an existing software for the retrieval of the aforementioned profiles in the atmosphere, estimate the measurement uncertainties, and perform a comparative analysis of the diffrent cases. The feasibility of using the obtained information for aerosol typing will be assessed.

The aim of the exercise is retrieval of atmospheric boundary layer height (ABLH) using etastic scattering lidar signals in near-real-time (NRT) and/or offline, as well as comparative analysis of diurnal variability of ABLH at selected site (rural, urban, peri-urban, costal, peatland, mountain, industrial). Lidar observations conducted during different field campaigns with different lidars/ceilometers can be used. Three main tasks are to be done. First, a technical one is to derive ABLH using wavelet method with existing software. Second, a scientific one is to explain the differences in ABLH in terms of the nocturnal (NL) and residual (RL) layers at night and the well-mixed (WML) layer at daytime and the diferent sites. Third one, it to compare the lidar-derived ABLH results with boundary layer derived form models (e.g. WRF, ECMWF, PALM).

The exercise is aimed at determination of the thermal conductivity of sand without sampling the test material. The measurement is made by linear probe technology. It is used in practice in situations when taking a sample of the material is inexpedient or technically impossible. This method is applied to investigate directly (in-situ) properties of cosmic bodies using automatic landing probes, e.g. comet Churyumov-Gerasimenko (mission Rosetta, experiment MUPUS).

Idea is the following: changes of the temperature of a long thin heater inserted in a solid material is a function of its thermal conductivity. When the heating power is known it is sufficient to register changes of the temperature. The latter can be done automatically.

Student is expected to perform 2 -3 measurements and analyze the source data.

Alternatively, student may process the existing source data.

This exercise is dedicated for beginner student.

The exercise aims to familiarize students with satellite remote sensing, widely used in atmospheric research. In this case, it is crucial to understand the design of the algorithms used for different detectors. As part of the classes, students will also conduct analyses of satellite data for a given case study (to be determined together with their supervisor) in terms of various parameters, such as atmospheric aerosols, cloudiness, and anthropogenic pollution.