Dr. Tatiana Podladchikova specializes in the development of innovative approaches in estimation theory to extract
a useful knowledge from noisy experimental data, control and forecasting for broad range of space applications:
solar physics, space weather, navigation and biomedical research and the development of fundamental tools
for analyzing and solving a broad class of estimation problems. Often advantages of developed data exploitation
tools to solve one problem, in particular, approaches to predictive modeling, image recognition, noise statistics identification,
and dynamical state estimation in conditions of uncertainty, lead to progress in interdisciplinary applications.
Solar activity forecasting, space weather, detection of active events on the Sun to mitigate hazards of space accidents and their consequences.
Development of adaptive techniques for tracking of moving objects.
– Geomagnetic Storm Forecasting Service StormFocus
StormFocus service provides advance warnings about the future geomagnetic storm magnitude on an hourly basis.
The forecast is based on real-time solar wind and interplanetary magnetic field data, provided by DSCOVR spacecraft,
which is located in L1 Sun-Earth libration point 1 500 000 km in front of Earth.
– Updated medium-term sunspot number prediction service
A universal technique that improves medium-term sunspot number prediction methods as they are monthly updated
using the last available observations of smoothed sunspot numbers using adaptive Kalman filter (updated every month).
- Forecast of the auroral oval boundaries and aurora borealis on Yamal island
Forecast based on real-time ACE solar wind and interplanetary magnetic field data and provides estimates of geomagnetic latitudes
of poleward and equatorward boundaries of the auroral oval, and equatorward boundary of the diffusive aurora for the next hour
(updated every hour).
1. Podladchikova T., , R.A.M. Van der Linden, and A.M. Veronig (2017), Sunspot number second differences as precursor
of the following 11-year sunspot cycle, The Astrophysical Journal, 850, 81, doi:10.3847/1538-4357/aa93ef.
Short description: Forecasting the strength of the sunspot cycle is highly important for many space weather applications.
We introduce a method to reliably calculate sunspot number second differences (SNSD) in order to quantify the short-term
variations of sunspot activity. We demonstrate a steady relationship between the SNSD dynamics in the early stage of the
declining phase of a given cycle and the strength of the following sunspot cycle. This finding may bear physical implications
on the underlying dynamo at work. On the basis of the developed predictive indicator, at the early stage of declining phase
of cycle 24, we predict that the sunspot cycle 25 will be weaker.
2. Podladchikova, T. V., Y. Y. Shprits, and A. C. Kellerman, (2016), Customizations of the Kalman Filter to Reconstruct
the Dynamics of Earth’s Radiation Belts using Satellite Measurements, Special issue of Space Research Institute
Russian Academy of Science “Practical aspects of Heliogeophysics”, series “Applied aspects of space weather”.
Short description: Adaptive Kalman filter on the basis of noise statistics identification using electron flux measurements
of Van Allen Probes is developed to reconstruct globally the state and evolution of Earth’s radiation belts.
A technique to determine the rate of radial diffusion and the direction of its propagation is proposed.
3. Podladchikova, T. V., Y. Y. Shprits, D. Kondrashov, and A. C. Kellerman (2014), Noise statistics identification for Kalman filtering
of the electron radiation belt observations I: Model errors, Journal of Geophysical Research: Space Physics, 119, doi:10.1002/2014JA019897.
Short description: The development of innovation tools to identify the errors in theoretical models of radiation belts.
This tool is used to improve our confidence in the results of radiation belt electron phase space density reconstruction
by an adaptive Kalman filter.
4. Podladchikova, T. V., Y. Y. Shprits, A. C. Kellerman, and D. Kondrashov (2014), Noise statistics identification for Kalman filtering
of the electron radiation belt observations: 2. Filtration and smoothing, Journal of Geophysical Research: Space Physics, 119, doi:10.1002/2014JA019898.
Short description: The development of identification technique of measurement errors to optimize the data assimilation
of sparse satellite data. This provides the accurate and reliable global reconstruction of radiation belt dynamics.
Further improvement of radiation belt reconstruction is achieved by the backward smoothing procedure applied
to the forward Kalman filter.
5. Podladchikova,T. V., and A. A. Petrukovich (2012), Extended geomagnetic storm forecast ahead of available solar wind
measurements, Space Weather, 10, S07001, doi:10.1029/2012SW000786.
Short description: Real-time space weather service that provides the forecast of the geomagnetic storm magnitude
for the next several hours based on real-time solar wind and interplanetary magnetic field data.
6. Podladchikova T., and R.A.M. Van der Linden (2012), Kalman Filter Technique for Improving Prediction of Smoothed
Monthly Sunspot Numbers. Solar Physics, 277 (2), 397-416 , doi:10.1007/s11207-011-9899-y.
Short description: Real-time space weather service that provides the medium-term forecasting of the sunspot number
for the next 12 months using adaptive Kalman filter
7. Podladchikova T., and R.A.M. Van der Linden (2011), An upper limit prediction of the peak sunspot number for solar cycle 24.
Space Weather and Space Climate, 1, A01, doi:10.1051/swsc/2011110013.
Short description: The prediction of the maximum sunspot number in solar cycle 24 once the epoch of the minimum of cycle 23
has passed (2008 January). According to the prediction the peak value of cycle 24 will not exceed 72.
2015 The International Alexander Chizhevsky medal for Space Weather and Space
Climate for major contributions to space weather research and/or services.
2015 Excellence in Service Award, Skolkovo Institute of Science and Technology,
in recognition of outstanding performance and contribution to Skoltech mission and values.
2015 Certificate of Appreciation, Skolkovo Institute of Science and Technology,
for the notable contribution and service on the Organizing Committee at Skoltech Poster Competition in 2014 and 2015.
2011 Bottle of French Champagne for the paper to be first published in Journal of Space
Weather and Space Climate, a link between all the communities involved in Space Weather and in Space Climate.
2009 Best poster at European Space Weather Week 6, Belgium.
September 2017 Public lecture “In arms of the star called Sun”, Skolkovo International Gymnasium
May 2017 Kultura TV, show “Nablyudatel”, “How the Sun changes the Earth: climate turns”
March 2017 Public lecture “Sun and Space Weather”, Experimentanium Science Museum, educational program “Scientists for kids”, Moscow.
September 2016 Public lecture “Where Does the Solar Wind Blow”, educational program for exhibition “Cosmos: birth of new age”, Moscow.
May 2016 Public lecture “In arms of the star called Sun”, The Spring School 2016 for the Oxford Russia Fund fellows.
February 2016 Public lecture “Space Weather Forecast: Yesterday, Today, Tomorrow”, Russian State Library for Young Adults, Moscow.
December 2015 Radio program “Science in Focus”, Echo of Moscow radio station, Russia.
November 2015 Tatiana Podladchikova, applied mathematician from Skoltech, was awarded with the prestigious
International Alexander Chizhevsky medal (English, Russian). Sk.ru and the electronic journal
‘RF Science and Technology‘ also covered the news.
April 2015 Public lecture “Hot Breath of the Sun” , Polytechnic Museum, Moscow (in Russian).
Course “Experimental data processing”
The course introduces students to practically useful approaches of data processing for control and forecasting.
The focus is on identifying the hidden and implicit features and regularities of dynamical processes using experimental data.
The course exposes data processing methods from multiple vantage points: standard data processing methods and their hidden
capacity to solve difficult problems; statistical methods based on state-space models; methods of extracting the regularities
of a process on the basis of identifying key parameters. The course addresses the problems in navigation, solar physics,
geomagnetism, space weather and biomedical research.
Course content and main topics: Experimental data processing
ФИО: Подладчикова Татьяна Владимировна
Занимаемая должность (должности): Старший преподаватель
Преподаваемые дисциплины: Анализ экспериментальных данныъ
Ученая степень: кандидат технических наук 2009
Ученое звание: нет
Наименование направления подготовки и/или специальности: прикладная математика
Данные о повышении квалификации и/или профессиональной переподготовке: нет
Общий стаж работы: Более 8 лет
Стаж работы по специальности: 8 лет