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   * [[#abstract|Abstract]]
+  * [[#tab-2026|Final Report 2026]]
   * [[#tab-2025|2025]]
   * [[#tab-2024|2024]]
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 \\
   * Campaign Second Winter (15.11.2022 - 05.06.2023), 2023b, [[https://doi.org/10.5439/2229846|https://doi.org/10.5439/2229846]], artwork Size: N/A Pages: N/A.
   * Ettrichrätz, V., N. Maherndl, N. Pfeifer, A. Kötsche, H. Kalesse-Los, and M. Maahn, 2024: Ice particle characterization with the VISSS - a case study and statistical results from several field campaigns, AGU Fall Meet. Abstr., 2024, A23N-5.
   * Kalesse-Los, H., M. Maahn, V. Ettrichratz, and A. Kotsche, 2023a: Characterization of Orography-Influenced Riming and Secondary Ice Production and Their Effects on Precipitation Rates Using Radar Polarimetry and Doppler Spectra (CORSIPP-SAIL), Tech. rep., Oak Ridge National Laboratory (ORNL), TN, United States, [[https://www.osti.gov/biblio/2242406|https://www.osti.gov/biblio/2242406]].
   * Kalesse-Los, H., M. Maahn, A. Kötsche, V. Ettrichrätz, and I. Steinke: Leipzig University W-Band Cloud Radar, Gothic (Colorado), SAIL.
   * Kötsche, A., A. Myagkov, L. von Terzi, M. Maahn, V. Ettrichrätz, T. Vogl, A. Ryzhkov, P. Bukovcic, D. Ori, and H. Kalesse-Los, 2025a: Investigating KDP signatures inside and below the dendritic growth layer with W-band Doppler Radar and in situ snowfall camera, Atmos. Meas. Tech. (accepted), 1–38, [[https://doi.org/10/g94nvk|doi:10/g94nvk]].
   * Kötsche, A., M. Maahn, V. Ettrichrätz, and H. Kalesse-Los, 2025b: Snow microphysical processes in orographic turbulence revealed by cloud radar and in situ snowfall camera observations, EGUsphere (submitted to ACP).
   * Kötsche, A., A. Myagkov, L. von Terzi, M. Maahn, V. Ettrichrätz, T. Vogl, A. Ryzhkov, P. Bukovcic, D. Ori, and H. Kalesse-Los, 2025: Investigating KDP signatures inside and below the dendritic growth layer with W-band Doppler Radar and in situ snowfall camera, EGUsphere, 1–38, [[https://doi.org/10.5194/egusphere-2025-734|https://doi.org/10.5194/egusphere-2025-734]].
   * Küchler, N., S. Kneifel, U. Löhnert, P. Kollias, H. Czekala, and T. Rose, 2017: A W-Band Radar–Radiometer System for Accurate and Continuous Monitoring of Clouds and Precipitation, Journal of Atmospheric and Oceanic Technology [[https://doi.org/10.1175/JTECH-D-17-0019.1|https://doi.org/10.1175/JTECH-D-17-0019.1]].
   * Maahn, M., V. Ettrichraetz, and I. Steinke, 2024a: VISSS Raw data from SAIL at Gothic from November 2022 to June 2023, [[https://doi.org/10.5439/2278627|https://doi.org/10.5439/2278627]].
   * Maahn, M., D. Moisseev, I. Steinke, N. Maherndl, and M. D. Shupe, 2024b: Introducing the Video In Situ Snowfall Sensor (VISSS), Atmos. Meas. Tech., 17, 899–919, [[https://doi.org/10.5194/amt-17-899-2024|https://doi.org/10.5194/amt-17-899-2024]].
   * Maahn, M., V. Ettrichraetz, and I. Steinke, 2024: VISSS raw data from SAIL at Gothic from November 2022 to June 2023. [[https://doi.org/10.5439/2278627|doi:10.5439/2278627]]. [[https://www.osti.gov/servlets/purl/2278627/|https://www.osti.gov/servlets/purl/2278627/]].
   * Maahn, M., and V. Ettrichrätz, 2025: Video In situ snowfall sensor (VISSS) data for Eriswil (2023-2024). [[https://doi.org/10.1594/PANGAEA.981222|doi:10.1594/PANGAEA.981222]]. [[https://doi.pangaea.de/10.1594/PANGAEA.981222|https://doi.pangaea.de/10.1594/PANGAEA.981222]].
   * Maherndl, N., A. Battaglia, A. Kötsche, and M. Maahn, 2025: Riming-dependent snowfall rate and ice water content retrievals for W-band cloud radar, Atmos. Meas. Tech., 18, 3287–3304, [[https://doi.org/10/g9vgvc|doi:10/g9vgvc]].
   * Ohneiser, K., M. Hartmann, H. Wex, P. Seifert, A. Hardt, A. Miller, K. Baudrexl, W. Thomas, V. Ettrichrätz, M. Maahn, T. Gaudek, W. Schimmel, F. Senf, H. Griesche, M. Radenz, and J. Henneberger, 2025: Ice-nucleating particle depletion in the wintertime boundary layer in the pre-alpine region during stratus cloud conditions, EGUsphere (in review for ACP), [[https://doi.org/10.5194/egusphere-2025-3675|doi:10.5194/egusphere-2025-3675]], [[https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3675/|https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3675/]].
   * Ohneiser, K., P. Seifert, W. Schimmel, F. Senf, T. Gaudek, M. Radenz, A. Teisseire, V. Ettrichrätz, T. Vogl, N. Maherndl, N. Pfeifer, J. Henneberger, A. J. Miller, N. Omanovic, C. Fuchs, H. Zhang, F. Ramelli, R. Spirig, A. Kötsche, H. Kalesse-Los, M. Maahn, H. Corden, A. Berne, M. Hajipour, H. Griesche, J. Hofer, R. Engelmann, A. Skupin, A. Ansmann, and H. Baars, 2025: Impact of seeder-feeder cloud interaction on precipitation formation: a case study based on extensive remote-sensing, in-situ and model data, Egusphere, 1–38, [[https://doi.org/10/g9qnrf|doi:10/g9qnrf]].
 \\
 </WRAP> /* ENDOF References WRAP */
+</WRAP>
+</pane>
+/* Final Report 2026 */
+<pane id="tab-2026">
+<WRAP BOX justify>
+The project CORSIPP is dedicated to understanding riming and secondary ice production (SIP) processes in complex terrain. For this, we operated an innovative simultaneous-transmission-simultaneous-reception (STSR) scanning W-band cloud radar (LIMRAD94) together with the video in situ snowfall sensor (VISSS) for one entire winter season in the Colorado Rocky Mountain. The instruments were a part of the Atmospheric Radiation Measurement (ARM) Surface Atmosphere Integrated Field Laboratory ([[https://sail.lbl.gov/|SAIL]]) campaign where a Ka-band (ARM-KAZR) and a X-band radar (from Colorado State University, CSU) were deployed. It examines radar signals revealing ice particle types and processes such as aggregation, riming, and secondary ice production (SIP).
+Results show that combining multiple radar variables improves identification of snow processes, though turbulence complicates interpretation. A turbulent air layer, driven by orography and wind, enhances aggregation, riming, and SIP, with ice multiplication in the turbulent layer strongest at $−13\degree \mathrm{C}$ to $−15\degree \mathrm{C}$ due to ice collisional fragmentation. At the surface, snowfall consists mainly of aggregates, rimed particles, and graupel, depending on temperature. SIP can contribute up to ~50 % of precipitation under favorable conditions. Long-term observations at the measurement site indicate decreasing snowfall and denser snow, likely linked to warming and reduced moisture. Climate change may further alter these processes and snowfall characteristics.
+<wc figure
+    src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:corsipp_final_report_2026_1.png"
+    width="800"
+    modifiers="natural fit"
+    title="Figure 1"
+    caption="The suspected dominating microphysical processes during the analyzed fall streaks in (Kötsche et al., 2025). The black arrows show for which temperature interval I assume a certain mechanism (marked with the respective letter) to be dominant. Ice particle images from VISSS (during the time of each fall streak) were used to visualize the possible particle types in each fall streak."
+    caption-align="left"
+    load-animation="zoom-in"
+    click-action="lightbox">
+=== Investigating polarimetric radar signatures ===
+Polarimetric radar variables (e.g., specific differential phase KDP, differential reflectivity ZDR) are sensitive to particle shape, size, and concentration and can provide insight into snow microphysical processes such as riming, aggregation, and SIP (see Fig. 1). However, their interpretation remains uncertain due to limited in situ validation. Addressing this research gap forms the first study during CORSIPP, published in Atmospheric Chemistry and Physics (ACP) (Kötsche et al., 2025).
+The analysis shows that W-band KDP is primarily sensitive to small anisotropic ice particles ($\mathrm{D} \lt 1.5 \mathrm{mm}$), while large aggregates ($\mathrm{D} \geq 2.5 \mathrm{mm}$) contribute only about 10–20 % near the surface. Combining KDP, ZDR, and spectrally resolved ZDR improves the identification of dominant processes. However, KDP remains complex and requires careful interpretation due to turbulence, particle size distribution variability, and non-Rayleigh effects.
+\\ \\
+=== Influence of orographic turbulence ===
+The influence of orographic turbulence on snow microphysical processes and surface snowfall, as well as its quantification using radar polarimetry, remains insufficiently quantified. Addressing this research gap forms the second study during CORSIPP, published in ACP (Kötsche et al., 2026).
+A turbulent layer was present during most precipitation events (September 2021-–May 2023), with its height controlled by terrain and wind, typically forming in the lee of Gothic Mountain. This layer enhances aggregation, riming, SIP, and sublimation, as shown by comparing radar observations above and below it (see Fig. 2).
+A novel approach combining LIMRAD94 KDP, VISSS particle data, and radar forward modeling enabled one of the first systematic quantifications of SIP in orographic turbulence. Ice multiplication peaks between $−13\degree \mathrm{C}$ and $−15\degree \mathrm{C}$ and decreases at warmer temperatures ($\gt −10\degree \mathrm{C}$), consistent with enhanced ice–ice collisions. Collisional fragmentation involving graupel and aggregates is identified as the dominant mechanism. At higher temperatures, reduced splinter production and increased riming lower IM, while aggregation partly masks IM by incorporating secondary ice into larger particles.
+<wc figure
+    src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:corsipp_final_report_2026_2.png"
+    width="800"
+    modifiers="natural fit"
+    title="Figure 2"
+    caption="Conceptual diagram of the suspected (dominant) microphysical processes inside the turbulent layer as displayed in (Kötsche et al., 2026). The impact of microphysical processes on radar and VISSS variables is also displayed qualitatively: Red downward facing arrows indicate a decrease of the respective variable, upward facing green arrows an increase. Note that for the radar variables Reflectivity ($Z_e$), Mean Doppler velocity (MDV), spectral differential reflectivity ($sZ_{\mathrm{DR_{max}}}$) and specific differential phase (KDP), we can measure the change inside the turbulent layer. VISSS variables total number concentration ($N_{\mathrm{tot}}$), complexity (C), mean mass weighted diameter ($D_{32}$) and normalized rime mass fraction (M) are measured below the turbulent layer. Their change due to the microphysical process is suspected based on the change of particle and PSD properties. If a variable is not depicted, no clear trend can be derived. The particle images shown were recorded by VISSS."
+    caption-align="left"
+    load-animation="zoom-in"
+    click-action="lightbox">
+=== Surface snowfall and climate implications ===
+Each type of ice particle reflects distinct growth pathways and environmental conditions within the cloud, contributing differently to the overall precipitation rate. Long-term changes in snowfall may therefore indicate shifts in the dominant microphysical regimes of snowfall formation. In a warming climate, temperature-dependent processes such as riming frequency and SIP are expected to change, potentially altering the characteristics of surface snowfall.
+A fuzzy logic approach to determine ice particle shape was developed. Surface snowfall rates during CORSIPP were found to be dominated by aggregates, rimed particles, and graupel, depending on temperature. Model estimates suggest that SIP in the turbulent layer can contribute substantially to precipitation (27–55 %) under favorable conditions. Long-term observations at Gothic since 1976 reveal decreasing snowfall and snow water equivalent (SWE), accompanied by increasing snow density. The decline in SWE is closely linked to reduced integrated water vapor within the dendritic growth layer, consistent with increased high-pressure influence possibly associated with consecutive La Niña events. Future warming may alter the vertical coupling between orographic turbulence and the dendritic growth layer, potentially modifying the efficiency of aggregation, riming, and SIP.
+\\  \\
+=== Deviations and limitations ===
+The omnipresent turbulence at the measurement site degraded the interpretability of polarimetric signals and inhibited the use of radar Doppler spectra-based retrieval techniques such as the liquid water detection tool VOODOO (Schimmel et al., 2020) or the cloud microphysics detection tool PEAKO (Kalesse et al., 2019; Vogl et al., 2024), effectively "blinding" the radar measurements inside the turbulent layer, where the main portion of snow microphysical processes occurred. LDR was not available for the most time because the ARM Ka-band radar was deployed in single-polarization mode. This made it impossible to apply the SIP detection method by Luke et al. (2021). The advance of the PAMTRA radar forward operator (Mech et al., 2020) could not be conducted due to time and personnel constraints. With the cold temperature scanner offline during parts of the project due to technical issues, the study primarily uses $40\degree$ constant-elevation scans. This turned out to be advantageous, allowing statistical analysis of polarimetric variables and supporting simulations for the WIVERN 94-GHz space-borne Doppler radar concept (Illingworth et al., 2018; Battaglia et al., 2022).
+\\ \\
+<WRAP leftalign>
+**References**:
+\\
+  * Battaglia, A., P. Martire, E. Caubet, L. Phalippou, F. Stesina, P. Kollias, and A. Illingworth, 2022: End to end simulator for the WIVERN W-band Doppler conically scanning spaceborne radar, Atmospheric Measurement Techniques, 2021, 1–31.
+  * Illingworth, A. J., A. Battaglia, J. Bradford, M. Forsythe, P. Joe, P. Kollias, K. Lean, M. Lori, J.-F. Mahfouf, and S. Melo, 2018: WIVERN: A new satellite concept to provide global in-cloud winds, precipitation, and cloud properties, Bulletin of the American Meteorological Society, 99(8), 1669–1687.
+  * Kalesse, H., T. Vogl, C. Paduraru, and E. Luke, 2019: Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm, Atmospheric Measurement Techniques, 12(8), 4591–4617.
+  * Kötsche, A., M. Maahn, V. Ettrichrätz, and H. Kalesse-Los, 2026: Snow microphysical processes in orographic turbulence revealed by cloud radar and in situ snowfall camera observations, Atmospheric Chemistry and Physics, 26(4), 3277–3297, [[https://doi.org/10.5194/acp-26-3277-2026|https://doi.org/10.5194/acp-26-3277-2026]].
+  * Kötsche, A., A. Myagkov, L. von Terzi, M. Maahn, V. Ettrichrätz, T. Vogl, A. Ryzhkov, P. Bukovcic, D. Ori, and H. Kalesse-Los, 2025: Investigating KDP signatures inside and below the dendritic growth layer with W-band Doppler radar and in situ snowfall camera observations, Atmospheric Chemistry and Physics, 25(20), 14045–14070, [[https://doi.org/10.5194/acp-25-14045-2025|https://doi.org/10.5194/acp-25-14045-2025]].
+  * Luke, E. P., F. Yang, P. Kollias, A. M. Vogelmann, and M. Maahn, 2021: New insights into ice multiplication using remote-sensing observations of slightly supercooled mixed-phase clouds in the Arctic, Proceedings of the National Academy of Sciences, 118(13), e2021387118, [[https://doi.org/10.1073/pnas.2021387118|https://doi.org/10.1073/pnas.2021387118]].
+  * Mech, M., M. Maahn, S. Kneifel, D. Ori, E. Orlandi, P. Kollias, V. Schemann, and S. Crewell, 2020: PAMTRA 1.0: The Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13(9), 4229–4251.
+  * Schimmel, W., H. Kalesse, and P. Seifert, 2020: VOODOO—A deep learning approach for reVealing supercOOled liquiD beyOnd lidar attenuatiOn, AGU Fall Meeting Abstracts, 2020, A061-0010.
+  * Vogl, T., M. Radenz, F. Ramelli, R. Gierens, and H. Kalesse-Los, 2024: PEAKO and peakTree: Tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations, Atmospheric Measurement Techniques, 17(22), 6547–6568, [[https://doi.org/10.5194/amt-17-6547-2024|https://doi.org/10.5194/amt-17-6547-2024]].
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 **References**:
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   * Vogl, T., M. Maahn, S. Kneifel, W. Schimmel, D. Moisseev, H. Kalesse-Los, 2011: Using artificial neural networks to predict riming from doppler cloud radar observations, Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022.
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