Precipitation phase uncertainty in cold region conceptual models resulting from meteorological forcing time-step intervals

Precipitation phase determination is a known source of uncertainty in surface-based hydrological, ecological, safety, and climate models. This is primarily due to the surface precipitation phase being a result of cloud and atmospheric properties not measured at surface meteorological or hydrological stations. Adding to the uncertainty, many conceptual hydrological models use a 24-h average air temperature to determine the precipitation phase. However, meteorological changes to atmospheric properties that control the precipitation phase often substantially change at sub-daily timescales. Model uncertainty (precipitation phase error) using air temperature (AT), dew-point temperature (DP), and wet-bulb temperature (WB) thresholds were compared using averaged and time of observation readings at 1-, 3-, 6-, 12-, and 24-h periods. Precipitation phase uncertainty grew 35 – 65% from the use of 1 – 24 h data. Within a sub-dataset of observations occurring between AT (cid:1) 6 and 6 (cid:3) C representing 57% of annual precipitation, misclassi ﬁ ed precipitation was 7.9% 1 h and 11.8% 24 h. Of note, there was also little difference between 1 and 3 h uncertainty, typical time steps for surface meteorological observations.

et al. b). In the Scandinavian Peninsula, 41.77% of annual precipitation occurred with air temperatures (AT) À3 to 5 C in Norway, and 38.49% in Sweden with station maximum and minimum of 61.12% and 20.08%, respectively (Grigg et al. ). This abundance of precipitation occurring in near-freezing temperatures leads to a fair amount of precipitation phase uncertainty within conceptual models. Therefore, it is logical to seek ways to decrease this precipitation phase uncertainty.
Incorrect precipitation phase determination can have a cascading negative effect on both rapid response (e.g., flooding and road maintenance) and longer-term (e.g., water supply and ecosystem response) hydrological models (Harpold et al. a). For instance, a massive snow event misclassified as rain in a model could result in: (1) a rapid response flood model indicating higher water levels due to a significant melt event which would not be observed or (2) energy loss and wetting of a modeled snowpack which would be unrepresentative of actual conditions. Rapid response models for flooding can be on a 15-min, 1-, 3-, or 6-h timescale to allow quick responses to heavy precipitation events. Other models do not require such high-temporal resolution and may have 24 h or even monthly timescales.
Conceptual hydrological models often use a set T RS calibrated over a large area regardless of changes in physiography, vegetation, or other characteristics that may affect local/regional average atmospheric conditions (Grigg et al. ). Precipitation phase at the ground surface is a result of microphysical processes (melting, freezing, condensation, evaporation, ice condensation, and sublimation) between hydrometers and the atmosphere they fall through (see Stewart ; Thériault & Stewart ). The use of a set T RS assumes that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant (Feiccabrino et al. ) and is, therefore, a source of precipitation phase uncertainty (Feiccabrino ). However, the use of 24-h average temperatures also assumes that atmospheric conditions over an area are static for a full day.
A 24 h time step does not account for many regular atmospheric changes two of which are: (1) diurnal changes in temperature which are affected by clear skies, overcast skies, partly cloudy skies, or changes in the cloud cover through the day and (2) frontal boundaries and troughs which separate air-masses with often vastly different atmospheric properties.
Typically, on a cloud-free day, the near-surface air and the boundary layer are warmed by incoming short-wave solar radiation and cooled overnight as long-wave radiation is emitted from Earth. On an overcast day, the incoming short-wave radiation is reduced by cloud cover, which in turn reduces daytime high temperatures, and nighttime long-wave radiation is reradiated by the clouds moderating nightly low temperatures. If clouds move over an area in the morning after a full night of cooling, the typical daily pattern will be disrupted by a cold night and a cool day. If clouds move over an area in the evening after a full day of warming, the daily pattern will be disrupted by a warm day and a cool night. Both examples above run a chance of not being adequately represented by a 24-h average temperature.
Many meteorological changes take place on sub-daily time steps, therefore making 24-h averaged meteorological inputs into a model unrepresentative (e.g., Figures 1-4).
Mixed-phase precipitation (4,782 observations) was excluded from the datasets due to a lack of information on rain/snow ratios and many prior studies disregarding this phase, e.g., Bartlett et al. (). Frozen precipitation (247 observations) was included as the liquid in this analysis.
However, frozen precipitation can be considered either       Table 1 | Temperature range (Range) between 90% snow fraction (90% SF) and 10% snow fraction (10% SF) and the percent misclassified precipitation occurring with temperatures cooler than À6, warmer than 6, and between À6 and 6 for air temperature (AT), dew-point temperature (DP), and wet-bulb temperature (WB) thresholds in 1, 3, 6, 12, and 24 h time-step  the use of WB is much lower than AT and DP in all temporal resolutions. All three temperature measurements have similar reductions in misclassified precipitation as time resolutions are decreased (Figure 7). For example, AT, DP, and WB all had 60% of the error reduction from 24 to 1 h occur with a time step decrease to 12 h.

RESULTS AND DISCUSSION
These results indicate that a majority of the daily variability in average temperature measurements affecting misclassified precipitation using T RS was eliminated by cutting a 24-h time period in half. Interestingly, for AT, DP and WB, every reduction in time step produced a reduction in misclassified precipitation (Figures 6 and 7).
These results ( Figure 6) along with many previous studies, e.g., Matsuo et al. (), have found WB to be a better indicator of the surface precipitation phase than AT.
Other studies, e.g., Marks et al. (), have found DP to be a better precipitation phase indicator than AT alone.
However, AT is still used in many models and is available at almost every station reporting environmental measurements. WB and DP require RH, and other observation elements not always measured by stations for their calculation. Due to availability issues for RH and the continued use of AT in many models, improvement of AT methods have elevated importance. However, RH methods to include WB, consistently identify precipitation phase better than AT in the model PPDS.
98% misclassified precipitation occurred in AT, WB, and DP temperatures between À6 and 6 C, for 1-, 3-, 6-, 12-, and 24-h datasets ( Table 1). The daily time resolution has the greatest T RS difference (Figure 8(a)), largest misclassified precipitation percentages (Figures 6 and 9), and greatest mixed precipitation range (90% SF-10% SF) (Table 1) for AT DP and WB. As time resolution increased toward 1 h (Figure 8(a)-8(e)), the T RS for DP warms from À1.9 to À0.7 C and T RS for AT cool slightly from 1.4 to 1.2 C, bringing AT and DP T RS closer to WB ≅ 0.0 C. As time resolutions increase from daily to hourly, misclassified precipitation decreases in each time step (Figure 9), while the mixed-phase temperature range (Table 1) stays steady or decreases. This leads to noticeable decreases ( Figure 10) and reductions in error ( Figure 11) for AT, DP, and WB.
Interestingly, the proportion of observations in each À6 and 6 C dataset for AT (57%), DP (63%), and WB (63%) remained constant while the percent misclassified precipitation (Figure 9), and the mixed-phase temperature range (90% SF-10% SF) ( One result of concern for further studies is the relatively large standard deviations ranging from one-third to one-
• In almost all cases, reducing the temporal resolution between 24, 12, 6, 3 and hourly meteorological forcing reduced misclassified precipitation. However, the most significant decreases were between 24 and 12 h.
Surprisingly, 60% of the decrease between 24 and 1-h time resolutions could be attained for AT, DP, and WB by only cutting the daily temporal resolution in half.
• It is here suggested that if attempting to reduce precipitation phase uncertainty in a cold region hydrological model with a daily air temperature time step, the best two options would be to either switch to using wet-bulb temperature or reduce the time step to 3 or 1 h for more representative meteorological forcing.